Search Results

Search found 415 results on 17 pages for 'transactional'.

Page 14/17 | < Previous Page | 10 11 12 13 14 15 16 17  | Next Page >

• Cloud Backup: Getting the Users' Backs Up

  - by Tony Davis

  On Wednesday last week, Microsoft announced that as of July 1, all data transfers into its Microsoft Azure cloud will be free (though you have to pay for transferring data out). On Thursday last week, SQL Azure in Western Europe went down. It was a relatively short outage, but since SQL Azure currently provides no easy way to take a standard backup of a database and store it locally, many people had no recourse but to wait patiently for their cloud-based app to resume. It seems that Microsoft are very keen encourage developers to move their data onto their cloud, but are developers ready to do it, given that such basic backup capabilities are lacking? Recently on Simple-Talk, Mike Mooney described a perfect use case for the Microsoft Cloud. They had a simple web-based application with a SQL Server backend; they could move the application to Windows Azure, and the data into SQL Azure and in the process free themselves from much of the hassle surrounding management and scaling of the hardware, network and so on. It was a great fit and yet it nearly didn't happen; lack of support for the BACKUP command almost proved a show-stopper. Of course, backups of Azure databases are always and have always been taken automatically, for disaster recovery purposes, but these are strictly on-cloud copies and as of now it is not possible to use them to them to restore a database to a particular point in time. It seems that none of those clever Microsoft people managed to predict the need to perform basic backups of Azure databases so that copies could be stored locally, outside the Azure universe. At the very least, as Mike points out, performing a local backup before a new deployment is more or less mandatory. Microsoft did at least note the sound of gnashing teeth and, as a stop-gap measure, offered SQL Azure Database Copy which basically allows you to create an online clone of your database, but this doesn't allow for storing local archives of the data. To that end MS has provided SQL Azure Import/Export, to package up and export a database and its data, using BACPACs. These BACPACs do not guarantee transactional consistency; for example, if a child table is modified after the parent is copied, then the copied database will be in inconsistent state (meaning, to add to the fun, BACPACs need to be created from a database copy). In any event, widespread problems with BACPAC's evil cousin, the DACPAC have been well-documented, and it seems likely that many will also give BACPAC the bum's rush. Finally, in a TechEd 2011 presentation tagged "SQL Azure Advanced Administration", it was announced that "backup and restore" were coming in the next SQL Azure CTP. And yet this still doesn't mean that we'll get simple backups as DBAs know and love them. What it does mean, at least, is the ability to restore any given database to a point in time within a 2-week window. For the time being, if you want a local copy of your data and don't want to brave the BACPAC, one is left with SSIS or BCP, creative use of schema and data comparison tools, or use of SQL Azure Backup (currently in beta) in order to perform this simple but vital task. Cheers, Tony.

  Read the article

• Planning in the Cloud - For Real

  - by jmorourke

  One of the hottest topics at Oracle OpenWorld 2012 this week is “the cloud”.  Over the past few years, Oracle has made major investments in cloud-based applications, including some acquisitions, and now has over 100 applications available through Oracle Cloud services.  At OpenWorld this week, Oracle announced seven new offerings delivered via the Oracle Cloud services platform, one of which is the Oracle Planning and Budgeting Cloud Service.  Based on Oracle Hyperion Planning, this service is the first of Oracle’s EPM applications to be to be offered in the Cloud.    This solution is targeted to organizations that are struggling with spreadsheets or legacy planning and budgeting applications, want to deploy a world class solution for financial planning and budgeting, but are constrained by IT resources and capital budgets. With the Oracle Planning and Budgeting Cloud Service, organizations can fast track their way to world-class financial planning, budgeting and forecasting – at cloud speed, with no IT infrastructure investments and with minimal IT resources. Oracle Hyperion Planning is a market-leading budgeting, planning and forecasting application that is used by over 3,300 organizations worldwide.  Prior to this announcement, Oracle Hyperion Planning was only offered on a license and maintenance basis.  It could be deployed on-premise, or hosted through Oracle On-Demand or third party hosting partners.  With this announcement, Oracle’s market-leading Hyperion Planning application will be available as a Cloud Service and through subscription-based pricing. This lowers the cost of entry and deployment for new customers and provides a scalable environment to support future growth. With this announcement, Oracle is the first major vendor to offer one of its core EPM applications as a cloud-based service.  Other major vendors have recently announced cloud-based EPM solutions, but these are only BI dashboards delivered via a cloud platform.   With this announcement Oracle is providing a market-leading, world-class financial budgeting, planning and forecasting as a cloud service, with the following advantages: ·                     Subscription-based pricing ·                     Available standalone or as an extension to Oracle Fusion Financials Cloud Service ·                     Implementation services available from Oracle and the Oracle Partner Network ·                     High scalability and performance ·                     Integrated financial reporting and MS Office interface ·                     Seamless integration with Oracle and non-Oracle transactional applications ·                     Provides customers with more options for their planning and budgeting deployment vs. strictly on-premise or cloud-only solution providers. The OpenWorld announcement of Oracle Planning and Budgeting Cloud Service is a preview announcement, with controlled availability expected in calendar year 2012.  For more information, check out the links below: Press Release Web site If you have any questions or need additional information, please feel free to contact me at [email protected].

  Read the article

• Java EE 7 Roadmap

  - by Linda DeMichiel

  The Java EE 6 Platform, released in December 2009, has seen great uptake from the community with its POJO-based programming model, lightweight Web Profile, and extension points. There are now 13 Java EE 6 compliant appserver implementations today! When we announced the Java EE 7 JSR back in early 2011, our plans were that we would release it by Q4 2012. This target date was slightly over three years after the release of Java EE 6, but at the same time it meant that we had less than two years to complete a fairly comprehensive agenda — to continue to invest in significant enhancements in simplification, usability, and functionality in updated versions of the JSRs that are currently part of the platform; to introduce new JSRs that reflect emerging needs in the community; and to add support for use in cloud environments. We have since announced a minor adjustment in our dates (to the spring of 2013) in order to accommodate the inclusion of JSRs of importance to the community, such as Web Sockets and JSON-P. At this point, however, we have to make a choice. Despite our best intentions, our progress has been slow on the cloud side of our agenda. Partially this has been due to a lack of maturity in the space for provisioning, multi-tenancy, elasticity, and the deployment of applications in the cloud. And partially it is due to our conservative approach in trying to get things "right" in view of limited industry experience in the cloud area when we started this work. Because of this, we believe that providing solid support for standardized PaaS-based programming and multi-tenancy would delay the release of Java EE 7 until the spring of 2014 — that is, two years from now and over a year behind schedule. In our opinion, that is way too long. We have therefore proposed to the Java EE 7 Expert Group that we adjust our course of action — namely, stick to our current target release dates, and defer the remaining aspects of our agenda for PaaS enablement and multi-tenancy support to Java EE 8. Of course, we continue to believe that Java EE is well-suited for use in the cloud, although such use might not be quite ready for full standardization. Even today, without Java EE 7, Java EE vendors such as Oracle, Red Hat, IBM, and CloudBees have begun to offer the ability to run Java EE applications in the cloud. Deferring the remaining cloud-oriented aspects of our agenda has several important advantages: It allows Java EE Platform vendors to gain more experience with their implementations in this area and thus helps us avoid risks entailed by trying to standardize prematurely in an emerging area. It means that the community won't need to wait longer for those features that are ready at the cost of those features that need more time. Because we have already laid some of the infrastructure for cloud support in Java EE 7, including resource definition metadata, improved security configuration, JPA schema generation, etc., it will allow us to expedite a Java EE 8 release. We therefore plan to target the Java EE 8 Platform release for the spring of 2015. This shift in the scope of Java EE 7 allows us to better retain our focus on enhancements in simplification and usability and to deliver on schedule those features that have been most requested by developers. These include the support for HTML 5 in the form of Web Sockets and JSON-P; the simplified JMS 2.0 APIs; improved Managed Bean alignment, including transactional interceptors; the JAX-RS 2.0 client API; support for method-level validation; a much more comprehensive expression language; and more. We feel strongly that this is the right thing to do, and we hope that you will support us in this proposed direction.

  Read the article

• Learn Many Languages

  - by Jeff Foster

  My previous blog, Deliberate Practice, discussed the need for developers to “sharpen their pencil” continually, by setting aside time to learn how to tackle problems in different ways. However, the Sapir-Whorf hypothesis, a contested and somewhat-controversial concept from language theory, seems to hold reasonably true when applied to programming languages. It states that: “The structure of a language affects the ways in which its speakers conceptualize their world.” If you’re constrained by a single programming language, the one that dominates your day job, then you only have the tools of that language at your disposal to think about and solve a problem. For example, if you’ve only ever worked with Java, you would never think of passing a function to a method. A good developer needs to learn many languages. You may never deploy them in production, you may never ship code with them, but by learning a new language, you’ll have new ideas that will transfer to your current “day-job” language. With the abundant choices in programming languages, how does one choose which to learn? Alan Perlis sums it up best. “A language that doesn‘t affect the way you think about programming is not worth knowing“ With that in mind, here’s a selection of languages that I think are worth learning and that have certainly changed the way I think about tackling programming problems. Clojure Clojure is a Lisp-based language running on the Java Virtual Machine. The unique property of Lisp is homoiconicity, which means that a Lisp program is a Lisp data structure, and vice-versa. Since we can treat Lisp programs as Lisp data structures, we can write our code generation in the same style as our code. This gives Lisp a uniquely powerful macro system, and makes it ideal for implementing domain specific languages. Clojure also makes software transactional memory a first-class citizen, giving us a new approach to concurrency and dealing with the problems of shared state. Haskell Haskell is a strongly typed, functional programming language. Haskell’s type system is far richer than C# or Java, and allows us to push more of our application logic to compile-time safety. If it compiles, it usually works! Haskell is also a lazy language – we can work with infinite data structures. For example, in a board game we can generate the complete game tree, even if there are billions of possibilities, because the values are computed only as they are needed. Erlang Erlang is a functional language with a strong emphasis on reliability. Erlang’s approach to concurrency uses message passing instead of shared variables, with strong support from both the language itself and the virtual machine. Processes are extremely lightweight, and garbage collection doesn’t require all processes to be paused at the same time, making it feasible for a single program to use millions of processes at once, all without the mental overhead of managing shared state. The Benefits of Multilingualism By studying new languages, even if you won’t ever get the chance to use them in production, you will find yourself open to new ideas and ways of coding in your main language. For example, studying Haskell has taught me that you can do so much more with types and has changed my programming style in C#. A type represents some state a program should have, and a type should not be able to represent an invalid state. I often find myself refactoring methods like this… void SomeMethod(bool doThis, bool doThat) { if (!(doThis ^ doThat)) throw new ArgumentException(“At least one arg should be true”); if (doThis) DoThis(); if (doThat) DoThat(); } …into a type-based solution, like this: enum Action { DoThis, DoThat, Both }; void SomeMethod(Action action) { if (action == Action.DoThis || action == Action.Both) DoThis(); if (action == Action.DoThat || action == Action.Both) DoThat(); } At this point, I’ve removed the runtime exception in favor of a compile-time check. This is a trivial example, but is just one of many ideas that I’ve taken from one language and implemented in another.

  Read the article

• WiX, MSDeploy and an appealing configuration/deployment paradigm

  - by alexhildyard

  I do a lot of application and server configuration; I've done this for many years and have tended to view the complexity of this strictly in terms of the complexity of the ultimate configuration to be deployed. For example, specific APIs aside, I would tend to regard installing a server certificate as a more complex activity than, say, copying a file or adding a Registry entry.My prejudice revolved around the idea of a sequential deployment script that not only had the explicit prescription to apply a specific server configuration, but also made the implicit presumption that the server in question was in a good known state. Scripts like this fail for hundreds of reasons -- the Default Website didn't exist; the application had already been deployed; the application had already been partially deployed and failed to rollback fully, and so on. And so the problem is that the more complex the configuration activity, the more scope for error in any individual part of that activity, and therefore the greater the chance the server in question will not end up at exactly the desired configuration level.Recently I was introduced to a completely different mindset, which, for want of a better turn of phrase, I will call the "make it so" mindset. It's extremely simple both to explain and to implement. In place of the head-down, imperative script you used to use, you substitute a set of checks -- much like exception handlers -- around each configuration activity, starting with a check of the current system state. Thus the configuration logic becomes: "IF these services aren't started then start them, and IF XYZ website doesn't exist then create it, and IF these shares don't exist then create them, and IF these shares aren't permissioned in some particular way, then permission them so." This works. Really well, in my experience. Scenario 1: You want to get a system into a good known state; it's already in a good known state; you quickly realise there is nothing to do.Scenario 2: You want to get the system into a good known state; your script is flawed or the system is bust; it cannot be put into that state. You know exactly where (at least part of) the problem is and why.Scenario 3: You want to get the system into a good known state; people are fiddling around with the system just now. That's fine. You do what you can, and later you come back and try it againScenario 4: No one wants to deploy anything; they want you to prove that the previous deployment was successful. So you re-run the deployment script with the "-WhatIf" flag. It reports that there was nothing to change. There's your proof.I mentioned two technologies in the title -- MSI and MSDeploy. I am thinking specifically of the conversation that took place here. Having worked with both technologies, I think Rob Mensching's response is appropriately nuanced, and in essence the difference is this: sometimes your target is either to achieve a specific new server state, or to rollback to a known good one. Then again, your target may be to configure what you can, and to understand what you can't. Implicitly MSDeploy's "rollback" is simply to redeploy the previous version, whereas a well-crafted MSI will actively put your system into that state without further intervention. Either way, if all goes well it will leave you with a system in one of two states, whereas MSDeploy could leave your system in one of many states. The key is that MSDeploy and MSI are complementary technologies; which suits you best depends as much on Operational guidance as your Configuration remit.What I wanted to say was that I have always been for atomic, transactional-based configuration, but having worked with the "make it so" paradigm, I have been favourably impressed by the actual results. I'm tempted to put a more technical post up on this in due course.

  Read the article

• Thinking Local, Regional and Global

  - by Apeksha Singh-Oracle

  The FIFA World Cup tournament is the biggest single-sport competition: it’s watched by about 1 billion people around the world. Every four years each national team’s manager is challenged to pull together a group players who ply their trade across the globe. For example, of the 23 members of Brazil’s national team, only four actually play for Brazilian teams, and the rest play in England, France, Germany, Spain, Italy and Ukraine. Each country’s national league, each team and each coach has a unique style. Getting all these “localized” players to work together successfully as one unit is no easy feat. In addition to $35 million in prize money, much is at stake – not least national pride and global bragging rights until the next World Cup in four years time. Achieving economic integration in the ASEAN region by 2015 is a bit like trying to create the next World Cup champion by 2018. The team comprises Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam. All have different languages, currencies, cultures and customs, rules and regulations. But if they can pull together as one unit, the opportunity is not only great for business and the economy, but it’s also a source of regional pride. BCG expects by 2020 the number of firms headquartered in Asia with revenue exceeding $1 billion will double to more than 5,000. Their trade in the region and with the world is forecast to increase to 37% of an estimated $37 trillion of global commerce by 2020 from 30% in 2010. Banks offering transactional banking services to the emerging market place need to prepare to repond to customer needs across the spectrum – MSMEs, SMEs, corporates and multi national corporations. Customers want innovative, differentiated, value added products and services that provide: • Pan regional operational independence while enabling single source of truth at a regional level • Regional connectivity and Cash & Liquidity  optimization • Enabling Consistent experience for their customers  by offering standardized products & services across all ASEAN countries • Multi-channel & self service capabilities / access to real-time information on liquidity and cash flows • Convergence of cash management with supply chain and trade finance While enabling the above to meet customer demands, the need for a comprehensive and robust credit management solution for effective regional banking operations is a must to manage risk. According to BCG, Asia-Pacific wholesale transaction-banking revenues are expected to triple to $139 billion by 2022 from $46 billion in 2012. To take advantage of the trend, banks will have to manage and maximize their own growth opportunities, compete on a broader scale, manage the complexity within the region and increase efficiency. They’ll also have to choose the right operating model and regional IT platform to offer: • Account Services • Cash & Liquidity Management • Trade Services & Supply Chain Financing • Payments • Securities services • Credit and Lending • Treasury services The core platform should be able to balance global needs and local nuances. Certain functions need to be performed at a regional level, while others need to be performed on a country level. Financial reporting and regulatory compliance are a case in point. The ASEAN Economic Community is in the final lap of its preparations for the ultimate challenge: becoming a formidable team in the global league. Meanwhile, transaction banks are designing their own hat trick: implementing a world-class IT platform, positioning themselves to repond to customer needs and establishing a foundation for revenue generation for years to come. Anand Ramachandran Senior Director, Global Banking Solutions Practice Oracle Financial Services Global Business Unit

  Read the article

• Concurrent Affairs

  - by Tony Davis

  I once wrote an editorial, multi-core mania, on the conundrum of ever-increasing numbers of processor cores, but without the concurrent programming techniques to get anywhere near exploiting their performance potential. I came to the.controversial.conclusion that, while the problem loomed for all procedural languages, it was not a big issue for the vast majority of programmers. Two years later, I still think most programmers don't concern themselves overly with this issue, but I do think that's a bigger problem than I originally implied. Firstly, is the performance boost from writing code that can fully exploit all available cores worth the cost of the additional programming complexity? Right now, with quad-core processors that, at best, can make our programs four times faster, the answer is still no for many applications. But what happens in a few years, as the number of cores grows to 100 or even 1000? At this point, it becomes very hard to ignore the potential gains from exploiting concurrency. Possibly, I was optimistic to assume that, by the time we have 100-core processors, and most applications really needed to exploit them, some technology would be around to allow us to do so with relative ease. The ideal solution would be one that allows programmers to forget about the problem, in much the same way that garbage collection removed the need to worry too much about memory allocation. From all I can find on the topic, though, there is only a remote likelihood that we'll ever have a compiler that takes a program written in a single-threaded style and "auto-magically" converts it into an efficient, correct, multi-threaded program. At the same time, it seems clear that what is currently the most common solution, multi-threaded programming with shared memory, is unsustainable. As soon as a piece of state can be changed by a different thread of execution, the potential number of execution paths through your program grows exponentially with the number of threads. If you have two threads, each executing n instructions, then there are 2^n possible "interleavings" of those instructions. Of course, many of those interleavings will have identical behavior, but several won't. Not only does this make understanding how a program works an order of magnitude harder, but it will also result in irreproducible, non-deterministic, bugs. And of course, the problem will be many times worse when you have a hundred or a thousand threads. So what is the answer? All of the possible alternatives require a change in the way we write programs and, currently, seem to be plagued by performance issues. Software transactional memory (STM) applies the ideas of database transactions, and optimistic concurrency control, to memory. However, working out how to break down your program into sufficiently small transactions, so as to avoid contention issues, isn't easy. Another approach is concurrency with actors, where instead of having threads share memory, each thread runs in complete isolation, and communicates with others by passing messages. It simplifies concurrent programs but still has performance issues, if the threads need to operate on the same large piece of data. There are doubtless other possible solutions that I haven't mentioned, and I would love to know to what extent you, as a developer, are considering the problem of multi-core concurrency, what solution you currently favor, and why. Cheers, Tony.

  Read the article

• Interview with Tomas Ulin at the MySQL Innovation Day

  - by Monica Kumar

  MySQL Innovation Day held on June 5, 2012 was a great event for the MySQL engineers, users and customers to gather, share and network. I was able to get a few minutes with Tomas Ulin, Vice President of MySQL Engineering at Oracle, to ask him some questions. Here are the highlights of my interview with Tomas. Monica: This was the first MySQL Innovation Day, correct?  Why now, what was the strategy behind hosting this kind of event? Tomas: In the last year, we have rolled out an incredible number of MySQL events worldwide – some targeted at developers that are new to MySQL and others for the MySQL savvy. At the MySQL Innovation Day, our first event of this kind,, we had a number of our key engineers presenting lightning talks delivering previews of key new features as well as discussing roadmap. Our goal is to keep an open dialogue with the MySQL community. In fact, we are hosting a two-day conference, another first, for the MySQL community called MySQL Connect on Sept. 29-30 in San Francisco. If you attended the MySQL Innovation Day and liked what we did, you are going to love MySQL Connect. We’ll have a lot more of our engineers and many users and community members presenting hour long sessions and hands on labs. Our engineers will be presenting new MySQL features as well offer previews of upcoming enhancements. Monica: What's the big take-away from today's MySQL Innovation Day? Tomas: I hope the most important takeaway for attendees was to see that Oracle has been driving, and continues to drive MySQL innovation with a steady stream of new great GA and Development Milestone releases. Monica: What were attendees most interested in? What feedback did they have? Tomas: Feedback from attendees was incredibly positive and encouraging. In particular, they liked the interaction with the MySQL engineers and were also excited about the new early access features in MySQL 5.6 and MySQL Cluster 7.3. In addition, sessions delivered by MySQL users like Facebook, Pinterest and Twitter were very well received. For example, Pinterest talked about using MySQL to scale from 0 to billions of page views/month, Twitter talked about “Scaling twitter with MySQL” and Facebook discussed the many options to implement MySQL master failover solutions. The presentations are already available for download while some of the session videos will be made available on the MySQL Innovation Day web page shortly. Monica: How would you distinguish the use of MySQL vs. Oracle Database? What key factors should customers consider? Tomas: MySQL and Oracle Database complement each other. They are very different products, best suited to different use cases. Customers can choose world-class solutions from Oracle to fulfill a variety of needs. MySQL is a great choice for enterprise web-based, custom and embedded apps. Oracle Database is the leading choice for enterprise packaged applications such as ERP, CRM as well as high-end data warehousing and business intelligence applications. Monica: What are the highlights of the current MySQL 5.6 Development Milestone Release and early access features for MySQL Cluster 7.3? Tomas: MySQL 5.6 development milestone release builds on MySQL 5.5 by improving: Optimizer for better Performance, Scalability Performance Schema for better instrumentation InnoDB for better transactional throughput Replication for higher availability, data integrity NoSQL options for more flexibility We announced some new early access features in MySQL 5.6, including binary log group commit. We also announced early access features in MySQL Cluster 7.3 including support for foreign key constraints. Monica: How do people get these releases? Tomas: You can access development milestone releases by going to: http://dev.mysql.com/downloads/mysqlThen select the “Development Release” tab. The MySQL Cluster 7.3 and other early access features can be downloaded at: http://labs.mysql.com Monica: What's coming up next for MySQL? Tomas: Our development team is working in overdrive, cranking out new features with community feedback. Don’t miss the MySQL Connect conference being held in San Francisco on Sept. 29 and 30th. My team and I will be there. I hope you can join us! Monica: Thank you for your time, Tomas. I look forward to seeing you at the MySQL Connect conference. To our followers, I hope you found this interview informative. I welcome your comments. Please stay tuned here for more updates on MySQL. Note: Monica Kumar is Senior Director of product marketing for Linux, Virtualization and MySQL at Oracle.

  Read the article

• Oracle Enterprise Data Quality: Ever Integration-ready

  - by Mala Narasimharajan

  It is closing in on a year now since Oracle’s acquisition of Datanomic, and the addition of Oracle Enterprise Data Quality (EDQ) to the Oracle software family. The big move has caused some big shifts in emphasis and some very encouraging excitement from the field.  To give an illustration, combined with a shameless promotion of how EDQ can help to give quick insights into your data, I did a quick Phrase Profile of the subject field of emails to the Global EDQ mailing list since it was set up last September. The results revealed a very clear theme:   Integration, Integration, Integration! As well as the important Siebel and Oracle Data Integrator (ODI) integrations, we have been asked about integration with a huge variety of Oracle applications, including EBS, Peoplesoft, CRM on Demand, Fusion, DRM, Endeca, RightNow, and more - and we have not stood still! While it would not have been possible to develop specific pre-integrations with all of the above within a year, we have developed a package of feature-rich out-of-the-box web services and batch processes that can be plugged into any application or middleware technology with ease. And with Siebel, they work out of the box. Oracle Enterprise Data Quality version 9.0.4 includes the Customer Data Services (CDS) pack – a ready set of standard processes with standard interfaces, to provide integrated: Address verification and cleansing  Individual matching Organization matching The services can are suitable for either Batch or Real-Time processing, and are enabled for international data, with simple configuration options driving the set of locale-specific dictionaries that are used. For example, large dictionaries are provided to support international name transcription and variant matching, including highly specialized handling for Arabic, Japanese, Chinese and Korean data. In total across all locales, CDS includes well over a million dictionary entries.   Excerpt from EDQ’s CDS Individual Name Standardization Dictionary CDS has been developed to replace the OEM of Informatica Identity Resolution (IIR) for attached Data Quality on the Oracle price list, but does this in a way that creates a ‘best of both worlds’ situation for customers, who can harness not only the out-of-the-box functionality of pre-packaged matching and standardization services, but also the flexibility of OEDQ if they want to customize the interfaces or the process logic, without having to learn more than one product. From a competitive point of view, we believe this stands us in good stead against our key competitors, including Informatica, who have separate ‘Identity Resolution’ and general DQ products, and IBM, who provide limited out-of-the-box capabilities (with a steep learning curve) in both their QualityStage data quality and Initiate matching products. Here is a brief guide to the main services provided in the pack: Address Verification and Standardization EDQ’s CDS Address Cleaning Process The Address Verification and Standardization service uses EDQ Address Verification (an OEM of Loqate software) to verify and clean addresses in either real-time or batch. The Address Verification processor is wrapped in an EDQ process – this adds significant capabilities over calling the underlying Address Verification API directly, specifically: Country-specific thresholds to determine when to accept the verification result (and therefore to change the input address) based on the confidence level of the API Optimization of address verification by pre-standardizing data where required Formatting of output addresses into the input address fields normally used by applications Adding descriptions of the address verification and geocoding return codes The process can then be used to provide real-time and batch address cleansing in any application; such as a simple web page calling address cleaning and geocoding as part of a check on individual data.     Duplicate Prevention Unlike Informatica Identity Resolution (IIR), EDQ uses stateless services for duplicate prevention to avoid issues caused by complex replication and synchronization of large volume customer data. When a record is added or updated in an application, the EDQ Cluster Key Generation service is called, and returns a number of key values. These are used to select other records (‘candidates’) that may match in the application data (which has been pre-seeded with keys using the same service). The ‘driving record’ (the new or updated record) is then presented along with all selected candidates to the EDQ Matching Service, which decides which of the candidates are a good match with the driving record, and scores them according to the strength of match. In this model, complex multi-locale EDQ techniques can be used to generate the keys and ensure that the right balance between performance and matching effectiveness is maintained, while ensuring that the application retains control of data integrity and transactional commits. The process is explained below: EDQ Duplicate Prevention Architecture Note that where the integration is with a hub, there may be an additional call to the Cluster Key Generation service if the master record has changed due to merges with other records (and therefore needs to have new key values generated before commit). Batch Matching In order to allow customers to use different match rules in batch to real-time, separate matching templates are provided for batch matching. For example, some customers want to minimize intervention in key user flows (such as adding new customers) in front end applications, but to conduct a more exhaustive match on a regular basis in the back office. The batch matching jobs are also used when migrating data between systems, and in this case normally a more precise (and automated) type of matching is required, in order to minimize the review work performed by Data Stewards.  In batch matching, data is captured into EDQ using its standard interfaces, and records are standardized, clustered and matched in an EDQ job before matches are written out. As with all EDQ jobs, batch matching may be called from Oracle Data Integrator (ODI) if required. When working with Siebel CRM (or master data in Siebel UCM), Siebel’s Data Quality Manager is used to instigate batch jobs, and a shared staging database is used to write records for matching and to consume match results. The CDS batch matching processes automatically adjust to Siebel’s ‘Full Match’ (match all records against each other) and ‘Incremental Match’ (match a subset of records against all of their selected candidates) modes. The Future The Customer Data Services Pack is an important part of the Oracle strategy for EDQ, offering a clear path to making Data Quality Assurance an integral part of enterprise applications, and providing a strong value proposition for adopting EDQ. We are planning various additions and improvements, including: An out-of-the-box Data Quality Dashboard Even more comprehensive international data handling Address search (suggesting multiple results) Integrated address matching The EDQ Customer Data Services Pack is part of the Enterprise Data Quality Media Pack, available for download at http://www.oracle.com/technetwork/middleware/oedq/downloads/index.html.

  Read the article

• NoSQL Memcached API for MySQL: Latest Updates

  - by Mat Keep

  With data volumes exploding, it is vital to be able to ingest and query data at high speed. For this reason, MySQL has implemented NoSQL interfaces directly to the InnoDB and MySQL Cluster (NDB) storage engines, which bypass the SQL layer completely. Without SQL parsing and optimization, Key-Value data can be written directly to MySQL tables up to 9x faster, while maintaining ACID guarantees. In addition, users can continue to run complex queries with SQL across the same data set, providing real-time analytics to the business or anonymizing sensitive data before loading to big data platforms such as Hadoop, while still maintaining all of the advantages of their existing relational database infrastructure. This and more is discussed in the latest Guide to MySQL and NoSQL where you can learn more about using the APIs to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database The native Memcached API is part of the MySQL 5.6 Release Candidate, and is already available in the GA release of MySQL Cluster. By using the ubiquitous Memcached API for writing and reading data, developers can preserve their investments in Memcached infrastructure by re-using existing Memcached clients, while also eliminating the need for application changes. Speed, when combined with flexibility, is essential in the world of growing data volumes and variability. Complementing NoSQL access, support for on-line DDL (Data Definition Language) operations in MySQL 5.6 and MySQL Cluster enables DevOps teams to dynamically update their database schema to accommodate rapidly changing requirements, such as the need to capture additional data generated by their applications. These changes can be made without database downtime. Using the Memcached interface, developers do not need to define a schema at all when using MySQL Cluster. Lets look a little more closely at the Memcached implementations for both InnoDB and MySQL Cluster. Memcached Implementation for InnoDB The Memcached API for InnoDB is previewed as part of the MySQL 5.6 Release Candidate. As illustrated in the following figure, Memcached for InnoDB is implemented via a Memcached daemon plug-in to the mysqld process, with the Memcached protocol mapped to the native InnoDB API. Figure 1: Memcached API Implementation for InnoDB With the Memcached daemon running in the same process space, users get very low latency access to their data while also leveraging the scalability enhancements delivered with InnoDB and a simple deployment and management model. Multiple web / application servers can remotely access the Memcached / InnoDB server to get direct access to a shared data set. With simultaneous SQL access, users can maintain all the advanced functionality offered by InnoDB including support for Foreign Keys, XA transactions and complex JOIN operations. Benchmarks demonstrate that the NoSQL Memcached API for InnoDB delivers up to 9x higher performance than the SQL interface when inserting new key/value pairs, with a single low-end commodity server supporting nearly 70,000 Transactions per Second. Figure 2: Over 9x Faster INSERT Operations The delivered performance demonstrates MySQL with the native Memcached NoSQL interface is well suited for high-speed inserts with the added assurance of transactional guarantees. You can check out the latest Memcached / InnoDB developments and benchmarks here You can learn how to configure the Memcached API for InnoDB here Memcached Implementation for MySQL Cluster Memcached API support for MySQL Cluster was introduced with General Availability (GA) of the 7.2 release, and joins an extensive range of NoSQL interfaces that are already available for MySQL Cluster Like Memcached, MySQL Cluster provides a distributed hash table with in-memory performance. MySQL Cluster extends Memcached functionality by adding support for write-intensive workloads, a full relational model with ACID compliance (including persistence), rich query support, auto-sharding and 99.999% availability, with extensive management and monitoring capabilities. All writes are committed directly to MySQL Cluster, eliminating cache invalidation and the overhead of data consistency checking to ensure complete synchronization between the database and cache. Figure 3: Memcached API Implementation with MySQL Cluster Implementation is simple: 1. The application sends reads and writes to the Memcached process (using the standard Memcached API). 2. This invokes the Memcached Driver for NDB (which is part of the same process) 3. The NDB API is called, providing for very quick access to the data held in MySQL Cluster’s data nodes. The solution has been designed to be very flexible, allowing the application architect to find a configuration that best fits their needs. It is possible to co-locate the Memcached API in either the data nodes or application nodes, or alternatively within a dedicated Memcached layer. The benefit of this flexible approach to deployment is that users can configure behavior on a per-key-prefix basis (through tables in MySQL Cluster) and the application doesn’t have to care – it just uses the Memcached API and relies on the software to store data in the right place(s) and to keep everything synchronized. Using Memcached for Schema-less Data By default, every Key / Value is written to the same table with each Key / Value pair stored in a single row – thus allowing schema-less data storage. Alternatively, the developer can define a key-prefix so that each value is linked to a pre-defined column in a specific table. Of course if the application needs to access the same data through SQL then developers can map key prefixes to existing table columns, enabling Memcached access to schema-structured data already stored in MySQL Cluster. Conclusion Download the Guide to MySQL and NoSQL to learn more about NoSQL APIs and how you can use them to scale new generations of web, cloud, mobile and social applications on the world's most widely deployed open source database See how to build a social app with MySQL Cluster and the Memcached API from our on-demand webinar or take a look at the docs Don't hesitate to use the comments section below for any questions you may have 

  Read the article

• The Complementary Roles of PLM and PIM

  - by Ulf Köster

  Oracle Product Value Chain Solutions (aka Enterprise PLM Solutions) are a comprehensive set of product management solutions that work together to provide Oracle customers with a broad array of capabilities to manage all aspects of product life: innovation, design, launch, and supply chain / commercialization processes beyond the capabilities and boundaries of traditional engineering-focused Product Lifecycle Management applications. They support companies with an integrated managed view across the product value chain: From Lab to Launch, From Farm to Fork, From Concept to Product to Customer, From Product Innovation to Product Design and Product Commercialization. Product Lifecycle Management (PLM) represents a broad suite of software solutions to improve product-oriented business processes and data. PLM success stories prove that PLM helps companies improve time to market, increase product-related revenue, reduce product costs, reduce internal costs and improve product quality. As a maturing suite of enterprise solutions, PLM is still evolving to realize the promise it can provide across all facets of a business and all phases of the product lifecycle. The vision for PLM includes everything from gathering early requirements for a product through multiple stages of the product lifecycle from product design, through commercialization and eventual product retirement or replacement. In discrete or process industries, PLM is typically more focused on Product Definition as items with respect to the technical view of a material or part, including specifications, bills of material and manufacturing data. With Agile PLM, this is specifically related to capabilities addressing Product Collaboration, Governance and Compliance, Product Quality Management, Product Cost Management and Engineering Collaboration. PLM today is mainly addressing key requirements in the early product lifecycle, in engineering changes or in the “innovation cycle”, and primarily adds value related to product design, development, launch and engineering change process. In short, PLM is the master for Product Definition, wherever manufacturing takes place. Product Information Management (PIM) is a product suite that has evolved in parallel to PLM. Product Information Management (PIM) can extend the value of PLM implementations by providing complementary tools and capabilities. More relevant in the area of Product Commercialization, the vision for PIM is to manage product information throughout an enterprise and supply chain to improve product-related knowledge management, information sharing and synchronization from multiple data sources. PIM success stories have shown the ability to provide multiple benefits, with particular emphasis on reducing information complexity and information management costs. Product Information in PIM is typically treated as the commercial view of a material or part, including sales and marketing information and categorization. PIM collects information from multiple manufacturing sites and multiple suppliers into its repository, but also provides integration tools to push the information back out to the other systems, serving as an active central repository with the aim to provide a holistic view on any product sold by a company (hence the name “Product Hub”). In short, PIM is the master of commercial Product Information. So PIM is quickly becoming mandatory because of its value in optimizing multichannel selling processes and relationships with customers, as you can see from the following table: Viewpoint PLM Current State PIM Key Benefits PIM adds to PLM Product Lifecycle Primarily R&D Front end Innovation Cycle Change process Primarily commercial / transactional state of lifecycle Provides a seamless information flow from design and manufacturing through the ultimate selling and servicing of products Data Primarily focused on “item” vs. “product” data Product structures Specifications Technical information Repository for all product information. Reaches out to entire enterprise and its various silos of product information and descriptions Provides a “trusted source” of accurate product information to the internal organization and trading partners Data Lifecycle Repository for all design iterations Historical information Released, current information, with version management and time stamping Provides a single location to track and audit historical product information Communication PLM release finished product to ERP PLM is the master for Product Definition Captures information from disparate sources, including in-house data stores Recognizes the reality of today’s data “mess” across information silos Provides the ability to package product information to its audience in the desired, relevant format to meet their exacting business requirements Departmental R&D Manufacturing Quality Compliance Procurement Strategic Marketing Focus on Marketing and Sales Gathering information from other Departments, multiple sites, multiple suppliers A singular enterprise solution that leverages existing information silos and data stores Supply Chain Multi-site internal collaboration Supplier collaboration Customer collaboration Works with customers, exchanges / data pools, and trading partners to provide relevant product information packaged the way the customer desires Provides ability to provide trading partners and internal customers with information in a manner they desire, continuously Tools Data Management Collaboration Innovation Management Cleansing Synchronization Hub functions Consistent, clean and complete commercial product information The goals of both PLM and PIM, put simply, are to help companies make more profit from their products. PLM and PIM solutions can be easily added as they share some of the same goals, while coming from two different perspectives: the definition of the product and the commercialization of the product. Both can serve as a form of product “system of record”, but take different approaches to delivering value. Oracle Product Value Chain solutions offer rich new strategies for executives to collectively leverage Agile PLM, Product Data Hub, together with Enterprise Data Quality for Products, and other industry leading Oracle applications to achieve further incremental value, like Oracle Innovation Management. This is unique on the market today.

  Read the article

• Hello With Oracle Identity Manager Architecture

  - by mustafakaya

  Hi, my name is Mustafa! I'm a Senior Consultant in Fusion Middleware Team and living in Istanbul,Turkey. I worked many various Java based software development projects such as end-to-end web applications, CRM , Telco VAS and integration projects.I want to share my experiences and research about Fusion Middleware Products in this column. Customer always wants best solution from software consultants or developers. Solution will be a code snippet or change complete architecture. We faced different requests according to the case of customer. In my posts i want to discuss Fusion Middleware Products Architecture or how can extend usability with apis or UI customization and more and I look forward to engaging with you on your experiences and thoughts on this.  In my first post, i will be discussing Oracle Identity Manager architecture  and i plan to discuss Oracle Identity Manager 11g features in next posts. Oracle Identity Manager System Architecture Oracle Identity Governance includes Oracle Identity Manager,Oracle Identity Analytics and Oracle Privileged Account Manager. I will discuss Oracle Identity Manager architecture in this post.  In basically, Oracle Identity Manager is a n-tier standard  Java EE application that is deployed on Oracle WebLogic Server and uses  a database .  Oracle Identity Manager presentation tier has three different screen and two different client. Identity Self Service and Identity System Administration are web-based thin client. Design Console is a Java Swing Client that communicates directly with the Business Service Tier.  Identity Self Service provides end-user operations and delegated administration features. System Administration provides system administration functions. And Design Console mostly use for development management operations such as  create and manage adapter and process form,notification , workflow desing, reconciliation rules etc. Business service tier is implemented as an Enterprise JavaBeans(EJB) application. So you can extense Oracle Identity Manager capabilities.  -The SMPL and EJB APIs allow develop custom plug-ins such as management roles or identities.  -Identity Services allow use core business capabilites of Oracle Identity Manager such as The User provisioning or reconciliation service. -Integration Services allow develop custom connectors or adapters for various deployment needs. -Platform Services allow use Entitlement Servers, Scheduler or SOA composites. The Middleware tier allows you using capabilites ADF Faces,SOA Suites, Scheduler, Entitlement Server and BI Publisher Reports. So OIM allows you to configure workflows uses Oracle SOA Suite or define authorization policies use with Oracle Entitlement Server. Also you can customization of OIM UI without need to write code and using ADF Business Editor  you can extend custom attributes to user,role,catalog and other objects. Data tiers; Oracle Identity Manager is driven by data and metadata which provides flexibility and adaptability to Oracle Identity Manager functionlities.  -Database has five schemas these are OIM,SOA,MDS,OPSS and OES. Oracle Identity Manager uses database to store runtime and configuration data. And all of entity, transactional and audit datas are stored in database. -Metadata Store; customizations and personalizations are stored in file-based repository or database-based repository.And Oracle Identity Manager architecture,the metadata is in Oracle Identity Manager database to take advantage of some of the advanced performance and availability features that this mode provides. -Identity Store; Oracle Identity Manager provides the ability to integrate an LDAP-based identity store into Oracle Identity Manager architecture.  Oracle Identity Manager uses the human workflow module of Oracle Service Oriented Architecture Suite. OIM connects to SOA using the T3 URL which is front-end URL for the SOA server.Oracle Identity Manager uses embedded Oracle Entitlement Server for authorization checks in OIM engine.  Several Oracle Identity Manager modules use JMS queues. Each queue is processed by a separate Message Driven Bean (MDB), which is also part of the Oracle Identity Manager application. Message producers are also part of the Oracle Identity Manager application. Oracle Identity Manager uses a scheduled jobs for some activities in the background.Some of scheduled jobs come with Out-Of-Box such as the disable users after the end date of the users or you can define your custom schedule jobs with Oracle Identity Manager APIs. You can use Oracle BI Publisher for reporting Oracle Identity Manager transactions or audit data which are in database. About me: Mustafa Kaya is a Senior Consultant in Oracle Fusion Middleware Team, living in Istanbul. Before coming to Oracle, he worked in teams developing web applications and backend services at a telco company. He is a Java technology enthusiast, software engineer and addicted to learn new technologies,develop new ideas. Follow Mustafa on Twitter,Connect on LinkedIn, and visit his site for Oracle Fusion Middleware related tips.

  Read the article

• New Replication, Optimizer and High Availability features in MySQL 5.6.5!

  - by Rob Young

  As the Product Manager for the MySQL database it is always great to announce when the MySQL Engineering team delivers another great product release.  As a field DBA and developer it is even better when that release contains improvements and innovation that I know will help those currently using MySQL for apps that range from modest intranet sites to the most highly trafficked web sites on the web.  That said, it is my pleasure to take my hat off to MySQL Engineering for today's release of the MySQL 5.6.5 Development Milestone Release ("DMR"). The new highlighted features in MySQL 5.6.5 are discussed here: New Self-Healing Replication ClustersThe 5.6.5 DMR improves MySQL Replication by adding Global Transaction Ids and automated utilities for self-healing Replication clusters.  Prior to 5.6.5 this has been somewhat of a pain point for MySQL users with most developing custom solutions or looking to costly, complex third-party solutions for these capabilities.  With 5.6.5 these shackles are all but removed by a solution that is included with the GPL version of the database and supporting GPL tools.  You can learn all about the details of the great, problem solving Replication features in MySQL 5.6 in Mat Keep's Developer Zone article.  New Replication Administration and Failover UtilitiesAs mentioned above, the new Replication features, Global Transaction Ids specifically, are now supported by a set of automated GPL utilities that leverage the new GTIDs to provide administration and manual or auto failover to the most up to date slave (that is the default, but user configurable if needed) in the event of a master failure. The new utilities, along with links to Engineering related blogs, are discussed in detail in the DevZone Article noted above. Better Query Optimization and ThroughputThe MySQL Optimizer team continues to amaze with the latest round of improvements in 5.6.5. Along with much refactoring of the legacy code base, the Optimizer team has improved complex query optimization and throughput by adding these functional improvements: Subquery Optimizations - Subqueries are now included in the Optimizer path for runtime optimization.  Better throughput of nested queries enables application developers to simplify and consolidate multiple queries and result sets into a single unit or work. Optimizer now uses CURRENT_TIMESTAMP as default for DATETIME columns - For simplification, this eliminates the need for application developers to assign this value when a column of this type is blank by default. Optimizations for Range based queries - Optimizer now uses ready statistics vs Index based scans for queries with multiple range values. Optimizations for queries using filesort and ORDER BY.  Optimization criteria/decision on execution method is done now at optimization vs parsing stage. Print EXPLAIN in JSON format for hierarchical readability and Enterprise tool consumption. You can learn the details about these new features as well all of the Optimizer based improvements in MySQL 5.6 by following the Optimizer team blog. You can download and try the MySQL 5.6.5 DMR here. (look under "Development Releases")  Please let us know what you think!  The new HA utilities for Replication Administration and Failover are available as part of the MySQL Workbench Community Edition, which you can download here .Also New in MySQL LabsAs has become our tradition when announcing DMRs we also like to provide "Early Access" development features to the MySQL Community via the MySQL Labs.  Today is no exception as we are also releasing the following to Labs for you to download, try and let us know your thoughts on where we need to improve:InnoDB Online OperationsMySQL 5.6 now provides Online ADD Index, FK Drop and Online Column RENAME.  These operations are non-blocking and will continue to evolve in future DMRs.  You can learn the grainy details by following John Russell's blog.InnoDB data access via Memcached API ("NotOnlySQL") - Improved refresh of an earlier feature releaseSimilar to Cluster 7.2, MySQL 5.6 provides direct NotOnlySQL access to InnoDB data via the familiar Memcached API. This provides the ultimate in flexibility for developers who need fast, simple key/value access and complex query support commingled within their applications.Improved Transactional Performance, ScaleThe InnoDB Engineering team has once again under promised and over delivered in the area of improved performance and scale.  These improvements are also included in the aggregated Spring 2012 labs release:InnoDB CPU cache performance improvements for modern, multi-core/CPU systems show great promise with internal tests showing:    2x throughput improvement for read only activity 6x throughput improvement for SELECT range Read/Write benchmarks are in progress More details on the above are available here. You can download all of the above in an aggregated "InnoDB 2012 Spring Labs Release" binary from the MySQL Labs. You can also learn more about these improvements and about related fixes to mysys mutex and hash sort by checking out the InnoDB team blog.MySQL 5.6.5 is another installment in what we believe will be the best release of the MySQL database ever.  It also serves as a shining example of how the MySQL Engineering team at Oracle leads in MySQL innovation.You can get the overall Oracle message on the MySQL 5.6.5 DMR and Early Access labs features here. As always, thanks for your continued support of MySQL, the #1 open source database on the planet!

  Read the article

• IBM Keynote: (hardware,software)–>{IBM.java.patterns}

  - by Janice J. Heiss

  On Sunday evening, September 30, 2012, Jason McGee, IBM Distinguished Engineer and Chief Architect Cloud Computing, along with John Duimovich IBM Distinguished Engineer and Java CTO, gave an information- and idea-rich keynote that left Java developers with much to ponder.Their focus was on the challenges to make Java more efficient and productive given the hardware and software environments of 2012. “One idea that is very interesting is the idea of multi-tenancy,” said McGee, “and how we can move up the spectrum. In traditional systems, we ran applications on dedicated middleware, operating systems and hardware. A lot of customers still run that way. Now people introduce hardware virtualization and share the hardware. That is good but there is a lot more we can do. We can share middleware and the application itself.” McGee challenged developers to better enable the Java language to function in these higher density models. He spoke about the need to describe patterns that help us grasp the full environment that an application needs, whether it’s a web or full enterprise application. Developers need to understand the resources that an application interacts with in a way that is simple and straightforward. The task is to then automate that deployment so that the complexity of infrastructure can be by-passed and developers can live in a simpler world where the cloud can automatically configure the needed environment. McGee argued that the key, something IBM has been working on, is to use a simpler pattern that allows a cloud-based architecture to embrace the entire infrastructure required for an application and make it highly available, scalable and able to recover from failure. The cloud-based architecture would automate the complexity of setting up and managing the infrastructure. IBM has been trying to realize this vision for customers so they can describe their Java application environment simply and allow the cloud to automate the deployment and management of applications. “The point,” explained McGee, “is to package the executable used to describe applications, to drop it into a shared system and let that system provide some intelligence about how to deploy and manage those applications.”John Duimovich on Improvements in JavaMcGee then brought onstage IBM’s Distinguished Engineer and CTO for Java, John Duimovich, who showed the audience ways to deploy Java applications more efficiently.Duimovich explained that, “When you run lots of copies of Java in the cloud or any hypervisor virtualized system, there are a lot of duplications of code and jar files. IBM has a facility called ‘shared classes’ where we put shared code, read only artefacts in a cache that is sharable across hypervisors.” By putting JIT code in ahead of time, he explained that the application server will use 20% less memory and operate 30% faster.  He described another example of how the JVM allows for the maximum amount of sharing that manages the tenants and file sockets and memory use through throttling and control. Duimovich touched on the “thin is in” model and IBM’s Liberty Profile and lightweight runtime for the cloud, which allows for greater efficiency in interacting with the cloud.Duimovich discussed the confusion Java developers experience when, for example, the hypervisor tells them that that they have 8 and then 4 and then 16 cores. “Because hypervisors are virtualized, they can change based on resource needs across the hypervisor layer. You may have 10 instances of an operation system and you may need to reallocate memory, " explained Duimovich.  He showed how to resize LPARs, reallocate CPUs and migrate applications as needed. He explained how application servers can resize thread pools and better use resources based on information from the hypervisors.Java Challenges in Hardware and SoftwareMcGee ended the keynote with a summary of upcoming hardware and software challenges for the Java platform. He noted that one reason developers love Java is it allows them to ignore differences in hardware. He stated that the most important things happening in hardware were in network and storage – in developments such as the speed of SSD, the exploitation of high-speed, low-latency networking, and recent developments such as storage-class memory, and non-volatile main memory. “So we are challenged to maintain the benefits of Java and the abstraction it provides from hardware while still exploiting the new innovations in hardware,” said McGee.McGee discussed transactional messaging applications where developers send messages transactionally persist a message to storage, something traditionally done by backing messages on spinning disks, something mostly outdated. “Now,” he pointed out, “we would use SSD and store it in Flash and get 70,000 messages a second. If we stored it using a PCI express-based flash memory device, it is still Flash but put on a PCI express bus on a card closer to the CPU. This way I get 300,000 messages a second and 25% improvement in latency.” McGee’s central point was that hardware has a huge impact on the performance and scalability of applications. New technologies are enabling developers to build classes of Java applications previously unheard of. “We need to be able to balance these things in Java – we need to maintain the abstraction but also be able to exploit the evolution of hardware technology,” said McGee. According to McGee, IBM's current focus is on systems wherein hardware and software are shipped together in what are called Expert Integrated Systems – systems that are pre-optimized, and pre-integrated together. McGee closed IBM’s engaging and thought-provoking keynote by pointing out that the use of Java in complex applications is increasingly being augmented by a host of other languages with strong communities around them – JavaScript, JRuby, Scala, Python and so forth. Java developers now must understand the strengths and weaknesses of such newcomers as applications increasingly involve a complex interconnection of languages.

  Read the article

• MDM for Tax Authorities

  - by david.butler(at)oracle.com

  In last week’s MDM blog, we discussed MDM in the Public Sector. I want to continue that thread. After all, no industry faces tougher data quality problems than governmental organizations, and few industries suffer more significant down side consequences to poor operations than local, state and federal governments. One key challenge area is taxation. Tax Authorities face a multitude of IT challenges. Firstly, the data used in tax calculations is increasing in volume and complexity. They must improve service by introducing multi-channel contact centers and self-service capabilities. Security concerns necessitate increasingly sophisticated data protection procedures. And cost constraints are driving Tax Authorities to rely on off-the-shelf software for many of their functional areas. Compounding these issues is the fact that the IT architectures in operation at most revenue and collections agencies are very complex. They typically include multiple, disparate operational and analytical systems across which the sum total of data about individual constituents is fragmented. To make matters more complicated, taxation is not carried out by a single jurisdiction, and often sources of income including employers, investments and other sources of taxable income and deductions must also be tracked and shared among tax authorities. Collectively, these systems are involved in tax assessment and collections, risk analysis, scoring, tracking, auditing and investigation case management. The Problem of Constituent Data Management The infrastructure described above makes it very difficult to create a consolidated representation of a given party. Differing formats and data models mean that a constituent may be represented in one way in one system and in a different way in another. Individual records are frequently inaccurate, incomplete, out of date and/or inconsistent with other records relating to the same constituent. When constituent data must be aggregated and scored, information within each system must be rationalized and normalized so the agency can produce a constituent information file (CIF) that provides a single source of truth about that party. If information about that constituent changes, each system in turn must be updated. There have been many attempts to solve this problem with technology: from consolidating transactional systems to conducting manual systems integration projects and superimposing layers of business intelligence and analytics. All these approaches can be successful in solving a portion of the problem at a specific point in time, but without an enterprise perspective, anything gained is quickly lost again. Oracle Constituent Data Mastering for Tax Authorities: A Single View of the Constituent Oracle has a flexible and long-term solution to the problem of securely integrating and managing constituent data. The Oracle Solution for mastering Constituent Data for Tax Authorities is based on two core product offerings: Oracle Customer Hub and – optionally – Oracle Application Integration Architecture (AIA). Customer Hub is a master data management (MDM) product that centralizes, de-duplicates, and enriches constituent data. It unifies fragmented information without disrupting existing business processes or IT investments. Role based data access and privacy rules guarantee maximum security and privacy. Data is continuously and automatically synchronized with all source systems. With the Oracle Customer Hub managing the master constituent identity, every department can capture transaction activity against the same record, improving reporting accuracy, employee productivity, reliability of constituent analytics, and day-to-day constituent relationships. Oracle Application Integration Architecture provides a collection of core pre-built processes to support out of the box Master Data Governance across Oracle Customer Hub, Siebel CRM, and Oracle E-Business Suite. It also provides a framework to enable MDM integrations with other Oracle and non-Oracle applications. Oracle AIA removes some of the key inhibitors to implementing a service-oriented architecture (SOA) by providing a pre-built SOA-based middleware foundation as well as industry-optimized service oriented applications, all built around a SOA governance model that encourages effective design and reuse. I encourage you to read Oracle Solution for Mastering Constituents Data for Public Sector – Tax Authorities by Roberto Negro. It is an outstanding whitepaper that describes how the Oracle MDM solution allows you to create a unified, reconciled source of high-quality constituent data and gain an accurate single view of each constituent. This foundation enables you to lower the costs associated with data quality and integration and create a tax organization that is efficient, secure and constituent-centric. Also, don’t forget the upcoming webcast on Thursday, February 10th: Deliver Improved Services to Citizens at Lower Cost to your Organization Our Guest Speaker is Ruben Spekle, from Capgemini. He will also provide insight into Public Sector Master Data Management and Case Management implementations including one that was executed for a Dutch Government Agency. If you are interested in how governmental organizations from around the world are using MDM to advance their cause, click here to register for the webcast.

  Read the article

• Master Data Management Implementation Styles

  - by david.butler(at)oracle.com

  In any Master Data Management solution deployment, one of the key decisions to be made is the choice of the MDM architecture. Gartner and other analysts describe some different Hub deployment styles, which must be supported by a best of breed MDM solution in order to guarantee the success of the deployment project.   Registry Style: In a Registry Style MDM Hub, the various source systems publish their data and a subscribing Hub stores only the source system IDs, the Foreign Keys (record IDs on source systems) and the key data values needed for matching. The Hub runs the cleansing and matching algorithms and assigns unique global identifiers to the matched records, but does not send any data back to the source systems. The Registry Style MDM Hub uses data federation capabilities to build the "virtual" golden view of the master entity from the connected systems.   Consolidation Style: The Consolidation Style MDM Hub has a physically instantiated, "golden" record stored in the central Hub. The authoring of the data remains distributed across the spoke systems and the master data can be updated based on events, but is not guaranteed to be up to date. The master data in this case is usually not used for transactions, but rather supports reporting; however, it can also be used for reference operationally.   Coexistence Style: The Coexistence Style MDM Hub involves master data that's authored and stored in numerous spoke systems, but includes a physically instantiated golden record in the central Hub and harmonized master data across the application portfolio. The golden record is constructed in the same manner as in the consolidation style, and, in the operational world, Consolidation Style MDM Hubs often evolve into the Coexistence Style. The key difference is that in this architectural style the master data stored in the central MDM system is selectively published out to the subscribing spoke systems.   Transaction Style: In this architecture, the Hub stores, enhances and maintains all the relevant (master) data attributes. It becomes the authoritative source of truth and publishes this valuable information back to the respective source systems. The Hub publishes and writes back the various data elements to the source systems after the linking, cleansing, matching and enriching algorithms have done their work. Upstream, transactional applications can read master data from the MDM Hub, and, potentially, all spoke systems subscribe to updates published from the central system in a form of harmonization. The Hub needs to support merging of master records. Security and visibility policies at the data attribute level need to be supported by the Transaction Style hub, as well.   Adaptive Transaction Style: This is similar to the Transaction Style, but additionally provides the capability to respond to diverse information and process requests across the enterprise. This style emerged most recently to address the limitations of the above approaches. With the Adaptive Transaction Style, the Hub is built as a platform for consolidating data from disparate third party and internal sources and for serving unified master entity views to operational applications, analytical systems or both. This approach delivers a real-time Hub that has a reliable, persistent foundation of master reference and relationship data, along with all the history and lineage of data changes needed for audit and compliance tracking. On top of this persistent master data foundation, the Hub can dynamically aggregate transaction data on demand from different source systems to deliver the unified golden view to downstream systems. Data can also be accessed through batch interfaces, published to a message bus or served through a real-time services layer. New data sources can be readily added in this approach by extending the data model and by configuring the new source mappings and the survivorship rules, meaning that all legacy data hubs can be leveraged to contribute their records/rules into the new transaction hub. Finally, through rich user interfaces for data stewardship, it allows exception handling by business analysts to keep it current with business rules/practices while maintaining the reliability of best-of-breed master records.   Confederation Style: In this architectural style, several Hubs are maintained at departmental and/or agency and/or territorial level, and each of them are connected to the other Hubs either directly or via a central Super-Hub. Each Domain level Hub can be implemented using any of the previously described styles, but normally the Central Super-Hub is a Registry Style one. This is particularly important for Public Sector organizations, where most of the time it is practically or legally impossible to store in a single central hub all the relevant constituent information from all departments.   Oracle MDM Solutions can be deployed according to any of the above MDM architectural styles, and have been specifically designed to fully support the Transaction and Adaptive Transaction styles. Oracle MDM Solutions provide strong data federation and integration capabilities which are key to enabling the use of the Confederated Hub as a possible architectural style approach. Don't lock yourself into a solution that cannot evolve with your needs. With Oracle's support for any type of deployment architecture, its ability to leverage the outstanding capabilities of the Oracle technology stack, and its open interfaces for non-Oracle technology stacks, Oracle MDM Solutions provide a low TCO and a quick ROI by enabling a phased implementation strategy.

  Read the article

• Oracle Fusion Middleware 11g next launch phase - what a week of product releases! Feedback from our

  - by Jürgen Kress

    Product releases: SOA Suite 11gR1 Patch Set 2 (PS2) BPM Suite 11gR1 Released Oracle JDeveloper 11g (11.1.1.3.0) (Build 5660) Oracle WebLogic Server 11gR1 (10.3.3) Oracle JRockit (4.0) Oracle Tuxedo 11gR1 (11.1.1.1.0) Enterprise Manager 11g Grid Control Release 1 (11.1.0.1.0) for Linux x86/x86-64 All Oracle Fusion Middleware 11gR1 Software Download   BPM Suite 11gR1 Released by Manoj Das Oracle BPM Suite 11gR1 became available for download from OTN and eDelivery. If you have been following our plans in this area, you know that this is the release unifying BEA ALBPM product, which became Oracle BPM10gR3, with the Oracle stack. Some of the highlights of this release are: BPMN 2.0 modeling and simulation Web based Process Composer for BPMN and Rules authoring Zero-code environment with full access to Oracle SOA Suite’s rich set of application and other adapters Process Spaces – Out-of-box integration with Web Center Suite Process Analytics – Native process cubes as well as integration with Oracle BAM You can learn more about this release from the documentation. Notes about downloading and installing Please note that Oracle BPM Suite 11gR1 is delivered and installed as part of SOA 11.1.1.3.0, which is a sparse release (only incremental patch). To install: Download and install SOA 11.1.1.2.0, which is a full release (you can find the bits at the above location) Download and install SOA 11.1.1.3.0 During configure step (using the Fusion Middleware configuration wizard), use the Oracle Business Process Management template supplied with the SOA Suite11g (11.1.1.3.0) If you plan to use Process Spaces, also install Web Center 11.1.1.3.0, which also is delivered as a sparse release and needs to be installed on top of Web Center 11.1.1.2.0   SOA Suite 11gR1 Patch Set 2 (PS2) released by Demed L'Her We just released SOA Suite 11gR1 Patch Set 2 (PS2)! You can download it as usual from: OTN (main platforms only) eDelivery (all platforms) 11gR1 PS2 is delivered as a sparse installer, that is to say that it is meant to be applied on the latest full install (11gR1 PS1). That’s great for existing PS1 users who simply need to apply the patch and run the patch assistant – but an extra step for new users who will first need to download SOA Suite 11gR1 PS1 (in addition to the PS2 patch). What’s in that release? Bug fixes of course but also several significant new features. Here is a short selection of the most significant ones: Spring component (for native Java extensibility and integration) SOA Partitions (to organize and manage your composites) Direct Binding (for transactional invocations to and from Oracle Service Bus) HTTP binding (for those of you trying to do away with SOAP and looking for simple GET and POST) Resequencer (for ordering out-of-order messages) WS Atomic Transactions (WS-AT) support (for propagation of transactions across heterogeneous environments) Check out the complete list of new features in PS2 for more (including links to the documentation for the above)! But maybe even more importantly we are also releasing Oracle Service Bus 11gR1 and BPM Suite 11gR1 at the same time – all on the same base platform (WebLogic Server 10.3.3)! (NB: it might take a while for all pages and caches to be updated with the new content so if you don’t find what you need today, try again soon!)   Are you Systems Integrations and Independent Software Vendors ready to adopt and to deliver? Make sure that you become trained: Local training calendars Register for the SOA Partner Community & Webcast www.oracle.com/goto/emea/soa What is your feedback?  Who installed the software? please feel free to share your experience at http://twitter.com/soacommunity #soacommunity Technorati Tags: SOA partner community ACE Directoris SOA Suite PS2 BPM11g First feedback from our ACE Directors and key Partners:   Now, these are great times to start the journey into BPM! Hajo Normann Reuse of components across the Oracle 11G Fusion Middleware stack, BPM just is one of the components plugging into the stack and reuses all other components. Mr. Leon Smiers With BPM11g, Oracle offers a very competitive product which will have a big effect on the IT market. Guido Schmutz We have real BPMN 2.0, which get's executed. No more transformation from business models to executable models - just press the run button... Torsten Winterberg Oracle BPM Suite 11g brings Out-of-box integration with WebCenter Suite and Oracle ADF development framework. Andrejus Baranovskis With the release of BPM Suite 11g, Oracle has defined new standards for Business Process platforms. Geoffroy de Lamalle With User Messaging Service you can let Soa Suite 11g do all your Messaging Edwin Biemond

  Read the article

• Imaging: Paper Paper Everywhere, but None Should be in Sight

  - by Kellsey Ruppel

  Author: Vikrant Korde, Technical Architect, Aurionpro's Oracle Implementation Services team My wedding photos are stored in several empty shoeboxes. Yes...I got married before digital photography was mainstream...which means I'm old. But my parents are really old. They have shoeboxes filled with vacation photos on slides (I doubt many of you have even seen a home slide projector...and I hope you never do!). Neither me nor my parents should have shoeboxes filled with any form of photographs whatsoever. They should obviously live in the digital world...with no physical versions in sight (other than a few framed on our walls). Businesses grapple with similar challenges. But instead of shoeboxes, they have file cabinets and warehouses jam packed with paper invoices, legal documents, human resource files, material safety data sheets, incident reports, and the list goes on and on. In fact, regulatory and compliance rules govern many industries, requiring that this paperwork is available for any number of years. It's a real challenge...especially trying to find archived documents quickly and many times with no backup. Which brings us to a set of technologies called Image Process Management (or simply Imaging or Image Processing) that are transforming these antiquated, paper-based processes. Oracle's WebCenter Content Imaging solution is a combination of their WebCenter suite, which offers a robust set of content and document management features, and their Business Process Management (BPM) suite, which helps to automate business processes through the definition of workflows and business rules. Overall, the solution provides an enterprise-class platform for end-to-end management of document images within transactional business processes. It's a solution that provides all of the capabilities needed - from document capture and recognition, to imaging and workflow - to effectively transform your ‘shoeboxes’ of files into digitally managed assets that comply with strict industry regulations. The terminology can be quite overwhelming if you're new to the space, so we've provided a summary of the primary components of the solution below, along with a short description of the two paths that can be executed to load images of scanned documents into Oracle's WebCenter suite. WebCenter Imaging (WCI): the electronic document repository that provides security, annotations, and search capabilities, and is the primary user interface for managing work items in the imaging solution SOA & BPM Suites (workflow): provide business process management capabilities, including human tasks, workflow management, service integration, and all other standard SOA features. It's interesting to note that there a number of 'jumpstart' processes available to help accelerate the integration of business applications, such as the accounts payable invoice processing solution for E-Business Suite that facilitates the processing of large volumes of invoices WebCenter Enterprise Capture (WEC): expedites the capture process of paper documents to digital images, offering high volume scanning and importing from email, and allows for flexible indexing options WebCenter Forms Recognition (WFR): automatically recognizes, categorizes, and extracts information from paper documents with greatly reduced human intervention WebCenter Content: the backend content server that provides versioning, security, and content storage There are two paths that can be executed to send data from WebCenter Capture to WebCenter Imaging, both of which are described below: 1. Direct Flow - This is the simplest and quickest way to push an image scanned from WebCenter Enterprise Capture (WEC) to WebCenter Imaging (WCI), using the bare minimum metadata. The WEC activities are defined below: The paper document is scanned (or imported from email). The scanned image is indexed using a predefined indexing profile. The image is committed directly into the process flow 2. WFR (WebCenter Forms Recognition) Flow - This is the more complex process, during which data is extracted from the image using a series of operations including Optical Character Recognition (OCR), Classification, Extraction, and Export. This process creates three files (Tiff, XML, and TXT), which are fed to the WCI Input Agent (the high speed import/filing module). The WCI Input Agent directory is a standard ingestion method for adding content to WebCenter Imaging, the process for doing so is described below: WEC commits the batch using the respective commit profile. A TIFF file is created, passing data through the file name by including values separated by "_" (underscores). WFR completes OCR, classification, extraction, export, and pulls the data from the image. In addition to the TIFF file, which contains the document image, an XML file containing the extracted data, and a TXT file containing the metadata that will be filled in WCI, are also created. All three files are exported to WCI's Input agent directory. Based on previously defined "input masks", the WCI Input Agent will pick up the seeding file (often the TXT file). Finally, the TIFF file is pushed in UCM and a unique web-viewable URL is created. Based on the mapping data read from the TXT file, a new record is created in the WCI application.  Although these processes may seem complex, each Oracle component works seamlessly together to achieve a high performing and scalable platform. The solution has been field tested at some of the largest enterprises in the world and has transformed millions and millions of paper-based documents to more easily manageable digital assets. For more information on how an Imaging solution can help your business, please contact [email protected] (for U.S. West inquiries) or [email protected] (for U.S. East inquiries). About the Author: Vikrant is a Technical Architect in Aurionpro's Oracle Implementation Services team, where he delivers WebCenter-based Content and Imaging solutions to Fortune 1000 clients. With more than twelve years of experience designing, developing, and implementing Java-based software solutions, Vikrant was one of the founding members of Aurionpro's WebCenter-based offshore delivery team. He can be reached at [email protected].

  Read the article

• Using Windows Previous Versions to access ZFS Snapshots (July 14, 2009)

  - by user12612012

  The Previous Versions tab on the Windows desktop provides a straightforward, intuitive way for users to view or recover files from ZFS snapshots.  ZFS snapshots are read-only, point-in-time instances of a ZFS dataset, based on the same copy-on-write transactional model used throughout ZFS.  ZFS snapshots can be used to recover deleted files or previous versions of files and they are space efficient because unchanged data is shared between the file system and its snapshots.  Snapshots are available locally via the .zfs/snapshot directory and remotely via Previous Versions on the Windows desktop. Shadow Copies for Shared Folders was introduced with Windows Server 2003 but subsequently renamed to Previous Versions with the release of Windows Vista and Windows Server 2008.  Windows shadow copies, or snapshots, are based on the Volume Snapshot Service (VSS) and, as the [Shared Folders part of the] name implies, are accessible to clients via SMB shares, which is good news when using the Solaris CIFS Service.  And the nice thing is that no additional configuration is required - it "just works". On Windows clients, snapshots are accessible via the Previous Versions tab in Windows Explorer using the Shadow Copy client, which is available by default on Windows XP SP2 and later.  For Windows 2000 and pre-SP2 Windows XP, the client software is available for download from Microsoft: Shadow Copies for Shared Folders Client. Assuming that we already have a shared ZFS dataset, we can create ZFS snapshots and view them from a Windows client. zfs snapshot tank/home/administrator@snap101zfs snapshot tank/home/administrator@snap102 To view the snapshots on Windows, map the dataset on the client then right click on a folder or file and select Previous Versions.  Note that Windows will only display previous versions of objects that differ from the originals.  So you may have to modify files after creating a snapshot in order to see previous versions of those files. The screenshot above shows various snapshots in the Previous Versions window, created at different times.  On the left panel, the .zfs folder is visible, illustrating that this is a ZFS share.  The .zfs setting can be toggled as desired, it makes no difference when using previous versions.  To make the .zfs folder visible: zfs set snapdir=visible tank/home/administrator To hide the .zfs folder: zfs set snapdir=hidden tank/home/administrator The following screenshot shows the Previous Versions panel when a file has been selected.  In this case the user is prompted to view, copy or restore the file from one of the available snapshots. As can be seen from the screenshots above, the Previous Versions window doesn't display snapshot names: snapshots are listed by snapshot creation time, sorted in time order from most recent to oldest.  There's nothing we can do about this, it's the way that the interface works.  Perhaps one point of note, to avoid confusion, is that the ZFS snapshot creation time isnot the same as the root directory creation timestamp. In ZFS, all object attributes in the original dataset are preserved when a snapshot is taken, including the creation time of the root directory.  Thus the root directory creation timestamp is the time that the directory was created in the original dataset. # ls -d% all /home/administrator         timestamp: atime         Mar 19 15:40:23 2009         timestamp: ctime         Mar 19 15:40:58 2009         timestamp: mtime         Mar 19 15:40:58 2009         timestamp: crtime         Mar 19 15:18:34 2009 # ls -d% all /home/administrator/.zfs/snapshot/snap101         timestamp: atime         Mar 19 15:40:23 2009         timestamp: ctime         Mar 19 15:40:58 2009         timestamp: mtime         Mar 19 15:40:58 2009         timestamp: crtime         Mar 19 15:18:34 2009 The snapshot creation time can be obtained using the zfs command as shown below. # zfs get all tank/home/administrator@snap101NAME                             PROPERTY  VALUEtank/home/administrator@snap101  type      snapshottank/home/administrator@snap101  creation  Mon Mar 23 18:21 2009 In this example, the dataset was created on March 19th and the snapshot was created on March 23rd. In conclusion, Shadow Copies for Shared Folders provides a straightforward way for users to view or recover files from ZFS snapshots.  The Windows desktop provides an easy to use, intuitive GUI and no configuration is required to use or access previous versions of files or folders. REFERENCES FOR MORE INFORMATION ZFS ZFS Learning Center Introduction to Shadow Copies of Shared Folders Shadow Copies for Shared Folders Client

  Read the article

• Application Composer: Exposing Your Customizations in BI Analytics and Reporting

  - by Richard Bingham

  Introduction This article explains in simple terms how to ensure the customizations and extensions you have made to your Fusion Applications are available for use in reporting and analytics. It also includes four embedded demo videos from our YouTube channel (if they don't appear check the browser address bar for a blocking shield icon). If you are new to Business Intelligence consider first reviewing our getting started article, and you can read more about the topic of custom subject areas in the documentation book Extending Sales. There are essentially four sections to this post. First we look at how custom fields added to standard objects are made available for reporting. Secondly we look at creating custom subject areas on the standard objects. Next we consider reporting on custom objects, starting with simple standalone objects, then child custom objects, and finally custom objects with relationships. Finally this article reviews how flexfields are exposed for reporting. Whilst this article applies to both Cloud/SaaS and on-premises deployments, if you are an on-premises developer then you can also use the BI Administration Tool to customize your BI metadata repository (the RPD) and create new subject areas. Whilst this is not covered here you can read more in Chapter 8 of the Extensibility Guide for Developers. Custom Fields on Standard Objects If you add a custom field to your standard object then it's likely you'll want to include it in your reports. This is very simple, since all new fields are instantly available in the "[objectName] Extension" folder in existing subject areas. The following two minute video demonstrates this. Custom Subject Areas for Standard Objects You can create your own subject areas for use in analytics and reporting via Application Composer. An example use-case could be to simplify the seeded subject areas, since they sometimes contain complex data fields and internal values that could confuse business users. One thing to note is that you cannot create subject areas in a sandbox, as it is not supported by BI, so once your custom object is tested and complete you'll need to publish the sandbox before moving forwards. The subject area creation processes is essentially two-fold. Once the request is submitted the ADF artifacts are generated, then secondly the related metadata is sent to the BI presentation server API's to make the updates there. One thing to note is that this second step may take up to ten minutes to complete. Once finished the status of the custom subject area request should show as 'OK' and it is then ready for use. Within the creation processes wizard-like steps there are three concepts worth highlighting: Date Flattening - this feature permits the roll up of reports at various date levels, such as data by week, month, quarter, or year. You simply check the box to enable it for that date field. Measures - these are your own functions that you can build into the custom subject area. They are related to the field data type and include min-max for dates, and sum(), avg(), and count() for  numeric fields. Implicit Facts - used to make the BI metadata join between your object fields and the calculated measure fields. The advice is to choose the most frequently used measure to ensure consistency. This video shows a simple example, where a simplified subject area is created for the customer 'Contact' standard object, picking just a few fields upon which users can then create reports. Custom Objects Custom subject areas support three types of custom objects. First is a simple standalone custom object and for which the same process mentioned above applies. The next is a custom child object created on a standard object parent, and finally a custom object that is related to a parent object - usually through a dynamic choice list. Whilst the steps in each of these last two are mostly the same, there are differences in the way you choose the objects and their fields. This is illustrated in the videos below.The first video shows the process for creating a custom subject area for a simple standalone custom object. This second video demonstrates how to create custom subject areas for custom objects that are of parent:child type, as well as those those with dynamic-choice-list relationships. &lt;span id=&quot;XinhaEditingPostion&quot;&gt;&lt;/span&gt; Flexfields Dynamic and Extensible Flexfields satisfy a similar requirement as custom fields (for Application Composer), with flexfields common across the Fusion Financials, Supply Chain and Procurement, and HCM applications. The basic principle is when you enable and configure your flexfields, in the edit page under each segment region (for both global and context segments) there is a BI Enabled check box. Once this is checked and you've completed your configuration, you run the Scheduled Process job named 'Import Oracle Fusion Data Extensions for Transactional Business Intelligence' to generate and migrate the related BI artifacts and data. This applies for dynamic, key, and extensible flexfields. Of course there is more to consider in terms of how you wish your flexfields to be implemented and exposed in your reports, and details are given in Chapter 4 of the Extending Applications guide.

  Read the article

• Microsoft Sql Server 2008 R2 System Databases

  For a majority of software developers little time is spent understanding the inner workings of the database management systems (DBMS) they use to store data for their applications.  I personally place myself in this grouping. In my case, I have used various versions of Microsoft’s SQL Server (2000, 2005, and 2008 R2) and just recently learned how valuable they really are when I was preparing to deliver a lecture on "SQL Server 2008 R2, System Databases". Microsoft Sql Server 2008 R2 System DatabasesSo what are system databases in MS SQL Server, and why should I know them? Microsoft uses system databases to support the SQL Server DBMS, much like a developer uses config files or database tables to support an application. These system databases individually provide specific functionality that allows MS SQL Server to function. Name Database File Log File Master master.mdf mastlog.ldf Resource mssqlsystemresource.mdf mssqlsystemresource.ldf Model model.mdf modellog.ldf MSDB msdbdata.mdf msdblog.ldf Distribution distmdl.mdf distmdl.ldf TempDB tempdb.mdf templog.ldf Master DatabaseIf you have used MS SQL Server then you should recognize the Master database especially if you used the SQL Server Management Studio (SSMS) to connect to a user created database. MS SQL Server requires the Master database in order for DBMS to start due to the information that it stores. Examples of data stored in the Master database User Logins Linked Servers Configuration information Information on User Databases Resource DatabaseHonestly, until recently I never knew this database even existed until I started to research SQL Server system databases. The reason for this is due largely to the fact that the resource database is hidden to users. In fact, the database files are stored within the Binn folder instead of the standard MS SQL Server database folder path. This database contains all system objects that can be accessed by all other databases.  In short, this database contains all system views and store procedures that appear in all other user databases regarding system information. One of the many benefits to storing system views and store procedures in a single hidden database is the fact it improves upgrading a SQL Server database; not to mention that maintenance is decreased since only one code base has to be mainlined for all of the system views and procedures. Model DatabaseThe Model database as the name implies is the model for all new databases created by users. This allows for predefining default database objects for all new databases within a MS SQL Server instance. For example, if every database created by a user needs to have an “Audit” table when it is  created then defining the “Audit” table in the model will guarantees that the table will be located in every new database create after the model is altered. MSDB DatabaseThe MSDBdatabase is used by SQL Server Agent, SQL Server Database Mail, SQL Server Service Broker, along with SQL Server. The SQL Server Agent uses this database to store job configurations and SQL job schedules along with SQL Alerts, and Operators. In addition, this database also stores all SQL job parameters along with each job’s execution history.  Finally, this database is also used to store database backup and maintenance plans as well as details pertaining to SQL Log shipping if it is being used. Distribution DatabaseThe Distribution database is only used during replication and stores meta data and history information pertaining to the act of replication data. Furthermore, when transactional replication is used this database also stores information regarding each transaction. It is important to note that replication is not turned on by default in MS SQL Server and that the distribution database is hidden from SSMS. Tempdb DatabaseThe Tempdb as the name implies is used to store temporary data and data objects. Examples of this include temp tables and temp store procedures. It is important to note that when using this database all data and data objects are cleared from this database when SQL Server restarts. This database is also used by SQL Server when it is performing some internal operations. Typically, SQL Server uses this database for the purpose of large sort and index operations. Finally, this database is used to store row versions if row versioning or snapsot isolation transactions are being used by SQL Server. Additionally, I would love to hear from others about their experiences using system databases, tables, and objects in a real world environments.

  Read the article

• Coherence Data Guarantees for Data Reads - Basic Terminology

  - by jpurdy

  When integrating Coherence into applications, each application has its own set of requirements with respect to data integrity guarantees. Developers often describe these requirements using expressions like "avoiding dirty reads" or "making sure that updates are transactional", but we often find that even in a small group of people, there may be a wide range of opinions as to what these terms mean. This may simply be due to a lack of familiarity, but given that Coherence sits at an intersection of several (mostly) unrelated fields, it may be a matter of conflicting vocabularies (e.g. "consistency" is similar but different in transaction processing versus multi-threaded programming). Since almost all data read consistency issues are related to the concept of concurrency, it is helpful to start with a definition of that, or rather what it means for two operations to be concurrent. Rather than implying that they occur "at the same time", concurrency is a slightly weaker statement -- it simply means that it can't be proven that one event precedes (or follows) the other. As an example, in a Coherence application, if two client members mutate two different cache entries sitting on two different cache servers at roughly the same time, it is likely that one update will precede the other by a significant amount of time (say 0.1ms). However, since there is no guarantee that all four members have their clocks perfectly synchronized, and there is no way to precisely measure the time it takes to send a given message between any two members (that have differing clocks), we consider these to be concurrent operations since we can not (easily) prove otherwise. So this leads to a question that we hear quite frequently: "Are the contents of the near cache always synchronized with the underlying distributed cache?". It's easy to see that if an update on a cache server results in a message being sent to each near cache, and then that near cache being updated that there is a window where the contents are different. However, this is irrelevant, since even if the application reads directly from the distributed cache, another thread update the cache before the read is returned to the application. Even if no other member modifies a cache entry prior to the local near cache entry being updated (and subsequently read), the purpose of reading a cache entry is to do something with the result, usually either displaying for consumption by a human, or by updating the entry based on the current state of the entry. In the former case, it's clear that if the data is updated faster than a human can perceive, then there is no problem (and in many cases this can be relaxed even further). For the latter case, the application must assume that the value might potentially be updated before it has a chance to update it. This almost aways the case with read-only caches, and the solution is the traditional optimistic transaction pattern, which requires the application to explicitly state what assumptions it made about the old value of the cache entry. If the application doesn't want to bother stating those assumptions, it is free to lock the cache entry prior to reading it, ensuring that no other threads will mutate the entry, a pessimistic approach. The optimistic approach relies on what is sometimes called a "fuzzy read". In other words, the application assumes that the read should be correct, but it also acknowledges that it might not be. (I use the qualifier "sometimes" because in some writings, "fuzzy read" indicates the situation where the application actually sees an original value and then later sees an updated value within the same transaction -- however, both definitions are roughly equivalent from an application design perspective). If the read is not correct it is called a "stale read". Going back to the definition of concurrency, it may seem difficult to precisely define a stale read, but the practical way of detecting a stale read is that is will cause the encompassing transaction to roll back if it tries to update that value. The pessimistic approach relies on a "coherent read", a guarantee that the value returned is not only the same as the primary copy of that value, but also that it will remain that way. In most cases this can be used interchangeably with "repeatable read" (though that term has additional implications when used in the context of a database system). In none of cases above is it possible for the application to perform a "dirty read". A dirty read occurs when the application reads a piece of data that was never committed. In practice the only way this can occur is with multi-phase updates such as transactions, where a value may be temporarily update but then withdrawn when a transaction is rolled back. If another thread sees that value prior to the rollback, it is a dirty read. If an application uses optimistic transactions, dirty reads will merely result in a lack of forward progress (this is actually one of the main risks of dirty reads -- they can be chained and potentially cause cascading rollbacks). The concepts of dirty reads, fuzzy reads, stale reads and coherent reads are able to describe the vast majority of requirements that we see in the field. However, the important thing is to define the terms used to define requirements. A quick web search for each of the terms in this article will show multiple meanings, so I've selected what are generally the most common variations, but it never hurts to state each definition explicitly if they are critical to the success of a project (many applications have sufficiently loose requirements that precise terminology can be avoided).

  Read the article

• Microsoft Sql Server 2008 R2 System Databases

  For a majority of software developers little time is spent understanding the inner workings of the database management systems (DBMS) they use to store data for their applications.  I personally place myself in this grouping. In my case, I have used various versions of Microsoft’s SQL Server (2000, 2005, and 2008 R2) and just recently learned how valuable they really are when I was preparing to deliver a lecture on "SQL Server 2008 R2, System Databases". Microsoft Sql Server 2008 R2 System DatabasesSo what are system databases in MS SQL Server, and why should I know them? Microsoft uses system databases to support the SQL Server DBMS, much like a developer uses config files or database tables to support an application. These system databases individually provide specific functionality that allows MS SQL Server to function. Name Database File Log File Master master.mdf mastlog.ldf Resource mssqlsystemresource.mdf mssqlsystemresource.ldf Model model.mdf modellog.ldf MSDB msdbdata.mdf msdblog.ldf Distribution distmdl.mdf distmdl.ldf TempDB tempdb.mdf templog.ldf Master DatabaseIf you have used MS SQL Server then you should recognize the Master database especially if you used the SQL Server Management Studio (SSMS) to connect to a user created database. MS SQL Server requires the Master database in order for DBMS to start due to the information that it stores. Examples of data stored in the Master database User Logins Linked Servers Configuration information Information on User Databases Resource DatabaseHonestly, until recently I never knew this database even existed until I started to research SQL Server system databases. The reason for this is due largely to the fact that the resource database is hidden to users. In fact, the database files are stored within the Binn folder instead of the standard MS SQL Server database folder path. This database contains all system objects that can be accessed by all other databases.  In short, this database contains all system views and store procedures that appear in all other user databases regarding system information. One of the many benefits to storing system views and store procedures in a single hidden database is the fact it improves upgrading a SQL Server database; not to mention that maintenance is decreased since only one code base has to be mainlined for all of the system views and procedures. Model DatabaseThe Model database as the name implies is the model for all new databases created by users. This allows for predefining default database objects for all new databases within a MS SQL Server instance. For example, if every database created by a user needs to have an “Audit” table when it is  created then defining the “Audit” table in the model will guarantees that the table will be located in every new database create after the model is altered. MSDB DatabaseThe MSDBdatabase is used by SQL Server Agent, SQL Server Database Mail, SQL Server Service Broker, along with SQL Server. The SQL Server Agent uses this database to store job configurations and SQL job schedules along with SQL Alerts, and Operators. In addition, this database also stores all SQL job parameters along with each job’s execution history.  Finally, this database is also used to store database backup and maintenance plans as well as details pertaining to SQL Log shipping if it is being used. Distribution DatabaseThe Distribution database is only used during replication and stores meta data and history information pertaining to the act of replication data. Furthermore, when transactional replication is used this database also stores information regarding each transaction. It is important to note that replication is not turned on by default in MS SQL Server and that the distribution database is hidden from SSMS. Tempdb DatabaseThe Tempdb as the name implies is used to store temporary data and data objects. Examples of this include temp tables and temp store procedures. It is important to note that when using this database all data and data objects are cleared from this database when SQL Server restarts. This database is also used by SQL Server when it is performing some internal operations. Typically, SQL Server uses this database for the purpose of large sort and index operations. Finally, this database is used to store row versions if row versioning or snapsot isolation transactions are being used by SQL Server. Additionally, I would love to hear from others about their experiences using system databases, tables, and objects in a real world environments.

  Read the article

• java 7 upgrade and hibernate annotation processor error

  - by Bill Turner

  I am getting the following warning, which seems to be triggering a subsequent warning and an error. I have been googling like mad, though have not found anything that makes it clear what it is I should do to resolve this. This issue occurs when I execute an Ant build. I am trying to migrate our project to Java 7. I have changed all the source='1.6' and target="1.6" to 1.7. I did find this related article: Forward compatible Java 6 annotation processor and SupportedSourceVersion It seems to indicate that I should build the Hibernate annotation processor jar myself, compiling it with with 1.7. It does not seem I should be required to do so. The latest version of the class in question (in hibernate-validator-annotation-processor-5.0.1.Final.jar) has been compiled with 1.6. Since the code in said class refers to SourceVersion.latestSupported(), and the 1.6 of that returns only RELEASE_6, there does not seem to be a generally available solution. Here is the warning: [javac] warning: Supported source version 'RELEASE_6' from annotation processor 'org.hibernate.validator.ap.ConstraintValidationProcessor' less than -source '1.7' And, here are the subsequent warnings/error. [javac] warning: No processor claimed any of these annotations: javax.persistence.PersistenceContext,javax.persistence.Column,org.codehaus.jackson.annotate.JsonIgnore,javax.persistence.Id,org.springframework.context.annotation.DependsOn,com.trgr.cobalt.infrastructure.datasource.Bucketed,org.codehaus.jackson.map.annotate.JsonDeserialize,javax.persistence.DiscriminatorColumn,com.trgr.cobalt.dataroom.authorization.secure.Secured,org.hibernate.annotations.GenericGenerator,javax.annotation.Resource,com.trgr.cobalt.infrastructure.spring.domain.DomainField,org.codehaus.jackson.annotate.JsonAutoDetect,javax.persistence.DiscriminatorValue,com.trgr.cobalt.dataroom.datasource.config.core.CoreTransactionMandatory,org.springframework.stereotype.Repository,javax.persistence.GeneratedValue,com.trgr.cobalt.dataroom.datasource.config.core.CoreTransactional,org.hibernate.annotations.Cascade,javax.persistence.Table,javax.persistence.Enumerated,org.hibernate.annotations.FilterDef,javax.persistence.OneToOne,com.trgr.cobalt.dataroom.datasource.config.core.CoreEntity,org.springframework.transaction.annotation.Transactional,com.trgr.cobalt.infrastructure.util.enums.EnumConversion,org.springframework.context.annotation.Configuration,com.trgr.cobalt.infrastructure.spring.domain.UpdatedFields,com.trgr.cobalt.infrastructure.spring.documentation.SampleValue,org.springframework.context.annotation.Bean,org.codehaus.jackson.annotate.JsonProperty,javax.persistence.Basic,org.codehaus.jackson.map.annotate.JsonSerialize,com.trgr.cobalt.infrastructure.spring.validation.Required,com.trgr.cobalt.dataroom.datasource.config.core.CoreTransactionNever,org.springframework.context.annotation.Profile,com.trgr.cobalt.infrastructure.spring.stereotype.Persistor,javax.persistence.Transient,com.trgr.cobalt.infrastructure.spring.validation.NotNull,javax.validation.constraints.Size,javax.persistence.Entity,javax.persistence.PrimaryKeyJoinColumn,org.hibernate.annotations.BatchSize,org.springframework.stereotype.Service,org.springframework.beans.factory.annotation.Value,javax.persistence.Inheritance [javac] error: warnings found and -Werror specified TIA!

  Read the article

• java.lang.NoClassDefFoundError: org/springframework/transaction/interceptor/TransactionInterceptor

  - by user1137146

  I am trying to integrate spring 3.1.1 with hibernate 4.0. This is my dispatcher-servlet.xml: <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:aop="http://www.springframework.org/schema/aop" xmlns:context="http://www.springframework.org/schema/context" xmlns:jee="http://www.springframework.org/schema/jee" xmlns:lang="http://www.springframework.org/schema/lang" xmlns:p="http://www.springframework.org/schema/p" xmlns:tx="http://www.springframework.org/schema/tx" xmlns:util="http://www.springframework.org/schema/util" xmlns:mvc="http://www.springframework.org/schema/mvc" xsi:schemaLocation="http://www.springframework.org/schema/lang http://www.springframework.org/schema/lang/spring-lang.xsd http://www.springframework.org/schema/jee http://www.springframework.org/schema/jee/spring-jee.xsd http://www.springframework.org/schema/aop http://www.springframework.org/schema/aop/spring-aop.xsd http://www.springframework.org/schema/mvc http://www.springframework.org/schema/mvc/spring-mvc-3.1.xsd http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/util http://www.springframework.org/schema/util/spring-util.xsd http://www.springframework.org/schema/tx http://www.springframework.org/schema/tx/spring-tx-3.1.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd"> <context:component-scan base-package="com.future.controllers" /> <context:annotation-config /> <context:component-scan base-package="com.future.services.menu" /> <context:component-scan base-package="com.future.dao" /> <bean id="dataSource" class="org.apache.commons.dbcp.BasicDataSource" destroy-method="close" p:driverClassName="com.mysql.jdbc.Driver" p:url="jdbc:mysql://localhost:3306/bar_visitor2" p:username="root" p:password=""/> <bean id="viewResolver" class="org.springframework.web.servlet.view.InternalResourceViewResolver"> <property name="viewClass" value="org.springframework.web.servlet.view.JstlView" /> <property name="prefix" value="/WEB-INF/views/" /> <property name="suffix" value=".jsp" /> </bean> <bean id="sessionFactory" class="org.springframework.orm.hibernate4.LocalSessionFactoryBean"> <property name="dataSource" ref="dataSource" /> <property name="configLocation"> <value>classpath:hibernate.cfg.xml</value> </property> </bean> <tx:annotation-driven /> <bean id="transactionManager" class="org.springframework.orm.hibernate4.HibernateTransactionManager"> <property name="sessionFactory" ref="sessionFactory" /> </bean> When I try to use @Transactional annotation I am getting an error java.lang.NoClassDefFoundError: org/springframework/transaction/interceptor/TransactionInterceptor. I checked my classpath and there is TransactionInterceptor.class. What am I doing wrong? Should I add something? Edit This is my lib folder:

  Read the article

< Previous Page | 10 11 12 13 14 15 16 17  | Next Page >