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  • Can't save data for a member in a data form

    - by RahulS
    Implied sharing is an old thing everyone knows the reasons and solutions of that, still little theory about that: With Essbase implied sharing, some members are shared even if you do not explicitly set them as shared. These members are implied shared members. When an implied share relationship is created, each implied member assumes the other member’s value. Essbase assumes (or implies) a shared member relationship in these situations: 1. A parent has only one child 2. A parent has only one child that consolidates to the parent In a Planning form that contains members with an implied sharing relationship, when a value is added for the parent, the child assumes the same value after the form is saved. Likewise, if a value is added for the child, the parent usually assumes the same value after a form is saved.For example, when a calculation script or load rule populates an implied share member, the other implied share member assumes the value of the member populated by the calculation script or load rule. The last value calculated or imported takes precedence. The result is the same whether you refer to the parent or the child as a variable in a calculation script. For more information have a look at: http://docs.oracle.com/cd/E17236_01/epm.1112/hp_admin_11122/ch14s11.html Now the issue which we are going to talk about is We loose data on save even when the parent is dynamic calc and has a single child. A dynamic calc parent to a single child:  If we design the form with following selection: In the data form we will find parent below the member and this is by design whenever you make a selection using commands to select all the member below parent, always children will appear before the parent: Lets try to enter data, Save it Now, try to change the way we selected members Here we go: Now the question again why this behavior: 1. Data from Planning data form passes to Essbase row by row, 2. Because in data form the child member appears before the parent, 3. First, data goes to Essbase for child (SingleStoreChild), 4. Then when Planning passes the data for parent there was #Missing or No data,  5. Over writes the data to #missing. PS: As we know that dynamic calc members are calculated on the fly they are not allocated with any memory in the Essbase, here the parent was dynamic calc and it was pointing to same memory as child in the background, when Planning was passing data to Essbase for second row it has updated the child with missing data.(Little confusing, let me know if you need more explanation) 6. As one of the solutions just change the order of appearance of parent and child. Cheers..!!! Rahul S. https://www.facebook.com/pages/HyperionPlanning/117320818374228

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  • Is Data Science “Science”?

    - by BuckWoody
    I hold the term “science” in very high esteem. I grew up on the Space Coast in Florida, and eventually worked at the Kennedy Space Center, surrounded by very intelligent people who worked in various scientific fields. Recently a new term has entered the computing dialog – “Data Scientist”. Since it’s not a standard term, it has a lot of definitions, and in fact has been disputed as a correct term. After all, the reasoning goes, if there’s no such thing as “Data Science” then how can there be a Data Scientist? This argument has been made before, albeit with a different term – “Computer Science”. In Peter Denning’s excellent article “Is Computer Science Science” (April  2005/Vol. 48, No. 4 COMMUNICATIONS OF THE ACM) there are many points that separate “science” from “engineering” and even “art”.  I won’t repeat the content of that article here (I recommend you read it on your own) but will leverage the points he makes there. Definition of Science To ask the question “is data science ‘science’” then we need to start with a definition of terms. Various references put the definition into the same basic areas: Study of the physical world Systematic and/or disciplined study of a subject area ...and then they include the things studied, the bodies of knowledge and so on. The word itself comes from Latin, and means merely “to know” or “to study to know”. Greek divides knowledge further into “truth” (episteme), and practical use or effects (tekhne). Normally computing falls into the second realm. Definition of Data Science And now a more controversial definition: Data Science. This term is so new and perhaps so niche that the major dictionaries haven’t yet picked it up (my OED reference is older – can’t afford to pop for the online registration at present). Researching the term's general use I created an amalgam of the definitions this way: “Studying and applying mathematical and other techniques to derive information from complex data sets.” Using this definition, data science certainly seems to be science - it's learning about and studying some object or area using systematic methods. But implicit within the definition is the word “application”, which makes the process more akin to engineering or even technology than science. In fact, I find that using these techniques – and data itself – part of science, not science itself. I leave out the concept of studying data patterns or algorithms as part of this discipline. That is actually a domain I see within research, mathematics or computer science. That of course is a type of science, but does not seek for practical applications. As part of the argument against calling it “Data Science”, some point to the scientific method of creating a hypothesis, testing with controls, testing results against the hypothesis, and documenting for repeatability.  These are not steps that we often take in working with data. We normally start with a question, and fit patterns and algorithms to predict outcomes and find correlations. In this way Data Science is more akin to statistics (and in fact makes heavy use of them) in the process rather than starting with an assumption and following on with it. So, is Data Science “Science”? I’m uncertain – and I’m uncertain it matters. Even if we are facing rampant “title inflation” these days (does anyone introduce themselves as a secretary or supervisor anymore?) I can tolerate the term at least from the intent that we use data to study problems across a wide spectrum, rather than restricting it to a single domain. And I also understand those who have worked hard to achieve the very honorable title of “scientist” who have issues with those who borrow the term without asking. What do you think? Science, or not? Does it matter?

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  • Data Modeling Resources

    - by Dejan Sarka
    You can find many different data modeling resources. It is impossible to list all of them. I selected only the most valuable ones for me, and, of course, the ones I contributed to. Books Chris J. Date: An Introduction to Database Systems – IMO a “must” to understand the relational model correctly. Terry Halpin, Tony Morgan: Information Modeling and Relational Databases – meet the object-role modeling leaders. Chris J. Date, Nikos Lorentzos and Hugh Darwen: Time and Relational Theory, Second Edition: Temporal Databases in the Relational Model and SQL – all theory needed to manage temporal data. Louis Davidson, Jessica M. Moss: Pro SQL Server 2012 Relational Database Design and Implementation – the best SQL Server focused data modeling book I know by two of my friends. Dejan Sarka, et al.: MCITP Self-Paced Training Kit (Exam 70-441): Designing Database Solutions by Using Microsoft® SQL Server™ 2005 – SQL Server 2005 data modeling training kit. Most of the text is still valid for SQL Server 2008, 2008 R2, 2012 and 2014. Itzik Ben-Gan, Lubor Kollar, Dejan Sarka, Steve Kass: Inside Microsoft SQL Server 2008 T-SQL Querying – Steve wrote a chapter with mathematical background, and I added a chapter with theoretical introduction to the relational model. Itzik Ben-Gan, Dejan Sarka, Roger Wolter, Greg Low, Ed Katibah, Isaac Kunen: Inside Microsoft SQL Server 2008 T-SQL Programming – I added three chapters with theoretical introduction and practical solutions for the user-defined data types, dynamic schema and temporal data. Dejan Sarka, Matija Lah, Grega Jerkic: Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft SQL Server 2012 – my first two chapters are about data warehouse design and implementation. Courses Data Modeling Essentials – I wrote a 3-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Logical and Physical Modeling for Analytical Applications – online course I wrote for Pluralsight. Working with Temporal data in SQL Server – my latest Pluralsight course, where besides theory and implementation I introduce many original ways how to optimize temporal queries. Forthcoming presentations SQL Bits 12, July 17th – 19th, Telford, UK – I have a full-day pre-conference seminar Advanced Data Modeling Topics there.

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  • OOW Session: Trends in Identity Management

    - by B Shashikumar
    In his Identity Management address today, Amit Jasuja - VP of Oracle Identity Management and Security Products, will discuss several key identity management trends and showcase how innovation is the key driver behind Oracle’s latest Identity Management 11gR2 release. This session will focus on how the latest 11gR2 release of Oracle Identity Management addresses emerging identity management requirements for mobile, social, and cloud computing. It also explores how existing Oracle Identity Management customers are simplifying implementations and reducing total cost of ownership. When: Monday, October 1, at 10:45 a.m., Where: Moscone West L3, room 3003, (session ID# CON9405) For a complete listing of all identity management sessions, hands-on labs, and more, check out the Focus on Identity Management now. 

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  • PostgreSQL to Data-Warehouse: Best approach for near-real-time ETL / extraction of data

    - by belvoir
    Background: I have a PostgreSQL (v8.3) database that is heavily optimized for OLTP. I need to extract data from it on a semi real-time basis (some-one is bound to ask what semi real-time means and the answer is as frequently as I reasonably can but I will be pragmatic, as a benchmark lets say we are hoping for every 15min) and feed it into a data-warehouse. How much data? At peak times we are talking approx 80-100k rows per min hitting the OLTP side, off-peak this will drop significantly to 15-20k. The most frequently updated rows are ~64 bytes each but there are various tables etc so the data is quite diverse and can range up to 4000 bytes per row. The OLTP is active 24x5.5. Best Solution? From what I can piece together the most practical solution is as follows: Create a TRIGGER to write all DML activity to a rotating CSV log file Perform whatever transformations are required Use the native DW data pump tool to efficiently pump the transformed CSV into the DW Why this approach? TRIGGERS allow selective tables to be targeted rather than being system wide + output is configurable (i.e. into a CSV) and are relatively easy to write and deploy. SLONY uses similar approach and overhead is acceptable CSV easy and fast to transform Easy to pump CSV into the DW Alternatives considered .... Using native logging (http://www.postgresql.org/docs/8.3/static/runtime-config-logging.html). Problem with this is it looked very verbose relative to what I needed and was a little trickier to parse and transform. However it could be faster as I presume there is less overhead compared to a TRIGGER. Certainly it would make the admin easier as it is system wide but again, I don't need some of the tables (some are used for persistent storage of JMS messages which I do not want to log) Querying the data directly via an ETL tool such as Talend and pumping it into the DW ... problem is the OLTP schema would need tweaked to support this and that has many negative side-effects Using a tweaked/hacked SLONY - SLONY does a good job of logging and migrating changes to a slave so the conceptual framework is there but the proposed solution just seems easier and cleaner Using the WAL Has anyone done this before? Want to share your thoughts?

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  • Collaborate 2010: Spotlight on Oracle Content Management

    - by [email protected]
    Excitement is building for the Collaborate conference April 18th through the 22nd. Outside of the event being in Las Vegas, which for me often seems to add to the excitement, there will be a great lineup of Oracle Content Management focused sessions. In fact, there are currently over 30 content management sessions scheduled, and attendees will get to hear from customers, partners, as well as Oracle experts. Attendees should expect to hear a lot about Oracle Content Management 11g at Collaborate 2010. Roel Stalman and Andy MacMillan will kick off these discussions on Monday, April 19th as they present Oracle Content Management's product strategy and roadmap (10:45 - 11:45). Monday's lineup also includes sessions on Oracle Imaging and Process Management (I/PM) 11g and Oracle Forms Recognition (2:30 - 3:30), which were both released in January. For those customers using older versions of I/PM or Stellent IBPM, be sure not to miss the "migrating to I/PM 11g" session on Monday as well (1:15 - 2:15) as this should give you some insight into the migration process. Check out the entire list of Oracle Content Management sessions here. Another focus at Collaborate this year is to discuss the benefits of using Oracle Content Management with Oracle Applications - Oracle E-Business Suite, PeopleSoft, and Siebel - so be sure to check out these sessions too: Accelerating Accounts Payable Processes with Integrated Document Imaging(Monday, April 19th, 3:45 - 4:45)Supercharge Your Siebel Sales and Marketing with Integrated Document Management(Tuesday, April 20th, 2:00 - 3:00)Oracle Enterprise 2.0 for Oracle Applications: The Value of an Integrated E2.0 Platform(Tuesday, April 20th, 3:15 - 4:15)Comprehensive Human Resources Automation with Oracle Content Management(Wednesday, April 21st, 1:00 - 2:00) Collaborate is also the perfect opportunity to meet Oracle executives and product experts. Attendees can sign up for 1 on 1 meetings at the event, and there will be someone representing each Oracle Content Management product. These meetings are probably the best way to get your product questions answered in a face-to-face manner. It seems more and more to me that Oracle Content Management customers are viewing Collaborate as "the" conference to attend each year. I hope you have plans to attend and I will see you there.

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  • Deploying Data Mining Models using Model Export and Import

    - by [email protected]
    In this post, we'll take a look at how Oracle Data Mining facilitates model deployment. After building and testing models, a next step is often putting your data mining model into a production system -- referred to as model deployment. The ability to move data mining model(s) easily into a production system can greatly speed model deployment, and reduce the overall cost. Since Oracle Data Mining provides models as first class database objects, models can be manipulated using familiar database techniques and technology. For example, one or more models can be exported to a flat file, similar to a database table dump file (.dmp). This file can be moved to a different instance of Oracle Database EE, and then imported. All methods for exporting and importing models are based on Oracle Data Pump technology and found in the DBMS_DATA_MINING package. Before performing the actual export or import, a directory object must be created. A directory object is a logical name in the database for a physical directory on the host computer. Read/write access to a directory object is necessary to access the host computer file system from within Oracle Database. For our example, we'll work in the DMUSER schema. First, DMUSER requires the privilege to create any directory. This is often granted through the sysdba account. grant create any directory to dmuser; Now, DMUSER can create the directory object specifying the path where the exported model file (.dmp) should be placed. In this case, on a linux machine, we have the directory /scratch/oracle. CREATE OR REPLACE DIRECTORY dmdir AS '/scratch/oracle'; If you aren't sure of the exact name of the model or models to export, you can find the list of models using the following query: select model_name from user_mining_models; There are several options when exporting models. We can export a single model, multiple models, or all models in a schema using the following procedure calls: BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODEL.dmp','dmdir','name =''MY_DT_MODEL'''); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODELS.dmp','dmdir',              'name IN (''MY_DT_MODEL'',''MY_KM_MODEL'')'); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('ALL_DMUSER_MODELS.dmp','dmdir'); END; A .dmp file can be imported into another schema or database using the following procedure call, for example: BEGIN   DBMS_DATA_MINING.IMPORT_MODEL('MY_MODELS.dmp', 'dmdir'); END; As with models from any data mining tool, when moving a model from one environment to another, care needs to be taken to ensure the transformations that prepare the data for model building are matched (with appropriate parameters and statistics) in the system where the model is deployed. Oracle Data Mining provides automatic data preparation (ADP) and embedded data preparation (EDP) to reduce, or possibly eliminate, the need to explicitly transport transformations with the model. In the case of ADP, ODM automatically prepares the data and includes the necessary transformations in the model itself. In the case of EDP, users can associate their own transformations with attributes of a model. These transformations are automatically applied when applying the model to data, i.e., scoring. Exporting and importing a model with ADP or EDP results in these transformations being immediately available with the model in the production system.

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  • Why Oracle Data Integrator for Big Data?

    - by Mala Narasimharajan
    Big Data is everywhere these days - but what exactly is it? It’s data that comes from a multitude of sources – not only structured data, but unstructured data as well.  The sheer volume of data is mindboggling – here are a few examples of big data: climate information collected from sensors, social media information, digital pictures, log files, online video files, medical records or online transaction records.  These are just a few examples of what constitutes big data.   Embedded in big data is tremendous value and being able to manipulate, load, transform and analyze big data is key to enhancing productivity and competitiveness.  The value of big data lies in its propensity for greater in-depth analysis and data segmentation -- in turn giving companies detailed information on product performance, customer preferences and inventory.  Furthermore, by being able to store and create more data in digital form, “big data can unlock significant value by making information transparent and usable at much higher frequency." (McKinsey Global Institute, May 2011) Oracle's flagship product for bulk data movement and transformation, Oracle Data Integrator, is a critical component of Oracle’s Big Data strategy. ODI provides automation, bulk loading, and validation and transformation capabilities for Big Data while minimizing the complexities of using Hadoop.  Specifically, the advantages of ODI in a Big Data scenario are due to pre-built Knowledge Modules that drive processing in Hadoop. This leverages the graphical UI to load and unload data from Hadoop, perform data validations and create mapping expressions for transformations.  The Knowledge Modules provide a key jump-start and eliminate a significant amount of Hadoop development.  Using Oracle Data Integrator together with Oracle Big Data Connectors, you can simplify the complexities of mapping, accessing, and loading big data (via NoSQL or HDFS) but also correlating your enterprise data – this correlation may require integrating across heterogeneous and standards-based environments, connecting to Oracle Exadata, or sourcing via a big data platform such as Oracle Big Data Appliance. To learn more about Oracle Data Integration and Big Data, download our resource kit to see the latest in whitepapers, webinars, downloads, and more… or go to our website on www.oracle.com/bigdata

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  • General Policies and Procedures for Maintaining the Value of Data Assets

    Here is a general list for policies and procedures regarding maintaining the value of data assets. Data Backup Policies and Procedures Backups are very important when dealing with data because there is always the chance of losing data due to faulty hardware or a user activity. So the need for a strategic backup system should be mandatory for all companies. This being said, in the real world some companies that I have worked for do not really have a good data backup plan. Typically when companies tend to take this kind of approach in data backups usually the data is not really recoverable.  Unfortunately when companies do not regularly test their backup plans they get a false sense of security because they think that they are covered. However, I can tell you from personal and professional experience that a backup plan/system is never fully implemented until it is regularly tested prior to the time when it actually needs to be used. Disaster Recovery Plan Expanding on Backup Policies and Procedures, a company needs to also have a disaster recovery plan in order to protect its data in case of a catastrophic disaster.  Disaster recovery plans typically encompass how to restore all of a company’s data and infrastructure back to a restored operational status.  Most Disaster recovery plans also include time estimates on how long each step of the disaster recovery plan should take to be executed.  It is important to note that disaster recovery plans are never fully implemented until they have been tested just like backup plans. Disaster recovery plans should be tested regularly so that the business can be confident in not losing any or minimal data due to a catastrophic disaster. Firewall Policies and Content Filters One way companies can protect their data is by using a firewall to separate their internal network from the outside. Firewalls allow for enabling or disabling network access as data passes through it by applying various defined restrictions. Furthermore firewalls can also be used to prevent access from the internal network to the outside by these same factors. Common Firewall Restrictions Destination/Sender IP Address Destination/Sender Host Names Domain Names Network Ports Companies can also desire to restrict what their network user’s view on the internet through things like content filters. Content filters allow a company to track what webpages a person has accessed and can also restrict user’s access based on established rules set up in the content filter. This device and/or software can block access to domains or specific URLs based on a few factors. Common Content Filter Criteria Known malicious sites Specific Page Content Page Content Theme  Anti-Virus/Mal-ware Polices Fortunately, most companies utilize antivirus programs on all computers and servers for good reason, virus have been known to do the following: Corrupt/Invalidate Data, Destroy Data, and Steal Data. Anti-Virus applications are a great way to prevent any malicious application from being able to gain access to a company’s data.  However, anti-virus programs must be constantly updated because new viruses are always being created, and the anti-virus vendors need to distribute updates to their applications so that they can catch and remove them. Data Validation Policies and Procedures Data validation is very important to ensure that only accurate information is stored. The existence of invalid data can cause major problems when businesses attempt to use data for knowledge based decisions and for performance reporting. Data Scrubbing Policies and Procedures Data scrubbing is valuable to companies in one of two ways. The first can be used to clean data prior to being analyzed for report generation. The second is that it allows companies to remove things like personally Identifiable information from its data prior to transmit it between multiple environments or if the information is sent to an external location. An example of this can be seen with medical records in regards to HIPPA laws that prohibit the storage of specific personal and medical information. Additionally, I have professionally run in to a scenario where the Canadian government does not allow any Canadian’s personal information to be stored on a server not located in Canada. Encryption Practices The use of encryption is very valuable when a company needs to any personal information. This allows users with the appropriated access levels to view or confirm the existence or accuracy of data within a system by either decrypting the information or encrypting a piece of data and comparing it to the stored version.  Additionally, if for some unforeseen reason the data got in to the wrong hands then they would have to first decrypt the data before they could even be able to read it. Encryption just adds and additional layer of protection around data itself. Standard Normalization Practices The use of standard data normalization practices is very important when dealing with data because it can prevent allot of potential issues by eliminating the potential for unnecessary data duplication. Issues caused by data duplication include excess use of data storage, increased chance for invalidated data, and over use of data processing. Network and Database Security/Access Policies Every company has some form of network/data access policy even if they have none. These policies help secure data from being seen by inappropriate users along with preventing the data from being updated or deleted by users. In addition, without a good security policy there is a large potential for data to be corrupted by unassuming users or even stolen. Data Storage Policies Data storage polices are very important depending on how they are implemented especially when a company is trying to utilize them in conjunction with other policies like Data Backups. I have worked at companies where all network user folders are constantly backed up, and if a user wanted to ensure the existence of a piece of data in the form of a file then they had to store that file in their network folder. Conversely, I have also worked in places where when a user logs on or off of the network there entire user profile is backed up. Training Policies One of the biggest ways to prevent data loss and ensure that data will remain a company asset is through training. The practice of properly train employees on how to work with in systems that access data is crucial when trying to ensure a company’s data will remain an asset. Users need to be trained on how to manipulate a company’s data in order to perform their tasks to reduce the chances of invalidating data.

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  • Importing Multiple Schemas to a Model in Oracle SQL Developer Data Modeler

    - by thatjeffsmith
    Your physical data model might stretch across multiple Oracle schemas. Or maybe you just want a single diagram containing tables, views, etc. spanning more than a single user in the database. The process for importing a data dictionary is the same, regardless if you want to suck in objects from one schema, or many schemas. Let’s take a quick look at how to get started with a data dictionary import. I’m using Oracle SQL Developer in this example. The process is nearly identical in Oracle SQL Developer Data Modeler – the only difference being you’ll use the ‘File’ menu to get started versus the ‘File – Data Modeler’ menu in SQL Developer. Remember, the functionality is exactly the same whether you use SQL Developer or SQL Developer Data Modeler when it comes to the data modeling features – you’ll just have a cleaner user interface in SQL Developer Data Modeler. Importing a Data Dictionary to a Model You’ll want to open or create your model first. You can import objects to an existing or new model. The easiest way to get started is to simply open the ‘Browser’ under the View menu. The Browser allows you to navigate your open designs/models You’ll see an ‘Untitled_1′ model by default. I’ve renamed mine to ‘hr_sh_scott_demo.’ Now go back to the File menu, and expand the ‘Data Modeler’ section, and select ‘Import – Data Dictionary.’ This is a fancy way of saying, ‘suck objects out of the database into my model’ Connect! If you haven’t already defined a connection to the database you want to reverse engineer, you’ll need to do that now. I’m going to assume you already have that connection – so select it, and hit the ‘Next’ button. Select the Schema(s) to be imported Select one or more schemas you want to import The schemas selected on this page of the wizard will dictate the lists of tables, views, synonyms, and everything else you can choose from in the next wizard step to import. For brevity, I have selected ALL tables, views, and synonyms from 3 different schemas: HR SCOTT SH Once I hit the ‘Finish’ button in the wizard, SQL Developer will interrogate the database and add the objects to our model. The Big Model and the 3 Little Models I can now see ALL of the objects I just imported in the ‘hr_sh_scott_demo’ relational model in my design tree, and in my relational diagram. Quick Tip: Oracle SQL Developer calls what most folks think of as a ‘Physical Model’ the ‘Relational Model.’ Same difference, mostly. In SQL Developer, a Physical model allows you to define partitioning schemes, advanced storage parameters, and add your PL/SQL code. You can have multiple physical models per relational models. For example I might have a 4 Node RAC in Production that uses partitioning, but in test/dev, only have a single instance with no partitioning. I can have models for both of those physical implementations. The list of tables in my relational model Wouldn’t it be nice if I could segregate the objects based on their schema? Good news, you can! And it’s done by default Several of you might already know where I’m going with this – SUBVIEWS. You can easily create a ‘SubView’ by selecting one or more objects in your model or diagram and add them to a new SubView. SubViews are just mini-models. They contain a subset of objects from the main model. This is very handy when you want to break your model into smaller, more digestible parts. The model information is identical across the model and subviews, so you don’t have to worry about making a change in one place and not having it propagate across your design. SubViews can be used as filters when you create reports and exports as well. So instead of generating a PDF for everything, just show me what’s in my ‘ABC’ subview. But, I don’t want to do any work! Remember, I’m really lazy. More good news – it’s already done by default! The schemas are automatically used to create default SubViews Auto-Navigate to the Object in the Diagram In the subview tree node, right-click on the object you want to navigate to. You can ask to be taken to the main model view or to the SubView location. If you haven’t already opened the SubView in the diagram, it will be automatically opened for you. The SubView diagram only contains the objects from that SubView Your SubView might still be pretty big, many dozens of objects, so don’t forget about the ‘Navigator‘ either! In summary, use the ‘Import’ feature to add existing database objects to your model. If you import from multiple schemas, take advantage of the default schema based SubViews to help you manage your models! Sometimes less is more!

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  • Data Warehouse: Modelling a future schedule

    - by Pat
    I'm creating a DW that will contain data on financial securities such as bonds and loans. These securities are associated with payment schedules. For example, a bond could pay quarterly, while a mortage would usually pay monthly (sometimes biweekly). The payment schedule is created when the security is traded and, in the majority of cases, will remain unchanged. However, the design would need to accomodate those cases where it does change. I'm currently attempting to model this data and I'm having difficulty coming up with a workable design. One of the most commonly queried fields is "next payment date". Users often want to know when a security will pay next. Therefore, I want to make it as easy as possible for them to get the next payment date and amount for each security. Also, users often run historical queries in which case they'd want the next payment date and amount as of a specific point in time. For example, they may want to look back at 1/31/09 and query the next payment dates (which would usually be in February 2009 for mortgages). It's also common that they want to query a security's entire payment schedule, which might consist of 360 records (30 year mortgage x 12 payments/year). Since the next payment date and amount would be changing each month or even biweekly, these fields wouldn't seem to fit into a slow-changing dimension very well. It would probably make more sense to use a fact table, but I'm unsure of how to model it. Any ideas would be greatly appreciated.

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  • Deploying Data Mining Models using Model Export and Import, Part 2

    - by [email protected]
    In my last post, Deploying Data Mining Models using Model Export and Import, we explored using DBMS_DATA_MINING.EXPORT_MODEL and DBMS_DATA_MINING.IMPORT_MODEL to enable moving a model from one system to another. In this post, we'll look at two distributed scenarios that make use of this capability and a tip for easily moving models from one machine to another using only Oracle Database, not an external file transport mechanism, such as FTP. The first scenario, consider a company with geographically distributed business units, each collecting and managing their data locally for the products they sell. Each business unit has in-house data analysts that build models to predict which products to recommend to customers in their space. A central telemarketing business unit also uses these models to score new customers locally using data collected over the phone. Since the models recommend different products, each customer is scored using each model. This is depicted in Figure 1.Figure 1: Target instance importing multiple remote models for local scoring In the second scenario, consider multiple hospitals that collect data on patients with certain types of cancer. The data collection is standardized, so each hospital collects the same patient demographic and other health / tumor data, along with the clinical diagnosis. Instead of each hospital building it's own models, the data is pooled at a central data analysis lab where a predictive model is built. Once completed, the model is distributed to hospitals, clinics, and doctor offices who can score patient data locally.Figure 2: Multiple target instances importing the same model from a source instance for local scoring Since this blog focuses on model export and import, we'll only discuss what is necessary to move a model from one database to another. Here, we use the package DBMS_FILE_TRANSFER, which can move files between Oracle databases. The script is fairly straightforward, but requires setting up a database link and directory objects. We saw how to create directory objects in the previous post. To create a database link to the source database from the target, we can use, for example: create database link SOURCE1_LINK connect to <schema> identified by <password> using 'SOURCE1'; Note that 'SOURCE1' refers to the service name of the remote database entry in your tnsnames.ora file. From SQL*Plus, first connect to the remote database and export the model. Note that the model_file_name does not include the .dmp extension. This is because export_model appends "01" to this name.  Next, connect to the local database and invoke DBMS_FILE_TRANSFER.GET_FILE and import the model. Note that "01" is eliminated in the target system file name.  connect <source_schema>/<password>@SOURCE1_LINK; BEGIN  DBMS_DATA_MINING.EXPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_SOURCE_DIR_OBJECT',                                 'name =''MY_MINING_MODEL'''); END; connect <target_schema>/<password>; BEGIN  DBMS_FILE_TRANSFER.GET_FILE ('MY_SOURCE_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '01.dmp',                               'SOURCE1_LINK',                               'MY_TARGET_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '.dmp' );  DBMS_DATA_MINING.IMPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_TARGET_DIR_OBJECT'); END; To clean up afterward, you may want to drop the exported .dmp file at the source and the transferred file at the target. For example, utl_file.fremove('&directory_name', '&model_file_name' || '.dmp');

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  • Abstract Data Type and Data Structure

    - by mark075
    It's quite difficult for me to understand these terms. I searched on google and read a little on Wikipedia but I'm still not sure. I've determined so far that: Abstract Data Type is a definition of new type, describes its properties and operations. Data Structure is an implementation of ADT. Many ADT can be implemented as the same Data Structure. If I think right, array as ADT means a collection of elements and as Data Structure, how it's stored in a memory. Stack is ADT with push, pop operations, but can we say about stack data structure if I mean I used stack implemented as an array in my algorithm? And why heap isn't ADT? It can be implemented as tree or an array.

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  • Unleash AutoVue on Your Unmanaged Data

    - by [email protected]
    Over the years, I've spoken to hundreds of customers who use AutoVue to collaborate on their "managed" data stored in content management systems, product lifecycle management systems, etc. via our many integrations. Through these conversations I've also learned a harsh reality - we will never fully move away from unmanaged data (desktops, file servers, emails, etc). If you use AutoVue today you already know that even if your primary use is viewing content stored in a content management system, you can still open files stored locally on your computer. But did you know that AutoVue actually has - built-in - a great solution for viewing, printing and redlining your data stored on file servers? Using the 'Server protocol' you can point AutoVue directly to a top-level location on any networked file server and provide your users with a link or shortcut to access an interface similar to the sample page shown below. Many customers link to pages just like this one from their internal company intranets. Through this webpage, users can easily search and browse through file server data with a 'click-and-view' interface to find the specific image, document, drawing or model they're looking for. Any markups created on a document will be accessible to everyone else viewing that document and of course real-time collaboration is supported as well. Customers on maintenance can consult the AutoVue Admin guide or My Oracle Support Doc ID 753018.1 for an introduction to the server protocol. Contact your local AutoVue Solutions Consultant for help setting up the sample shown above.

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  • Oracle-AmberPoint Webcast: Learn How Your Business Can Profit from the Combination

    - by jyothi.swaroop
    With the recent acquisition of AmberPoint, Oracle now offers an enhanced end-to-end SOA solution that features runtime governance, business transaction management, and cross-platform management capabilities. Put that solution to work and your business can achieve lower costs of implementation and higher profit. Join Ed Horst, Vice President, Oracle (former CMO of AmberPoint), and Ashish Mohindroo, Senior Director, Product Marketing, Oracle, as they discuss in this live Webcast the customer advantages of the Oracle and AmberPoint combination. Learn how our SOA solutions with AmberPoint capabilities can help you: Achieve more agility and visibility into your business processes Increase control and performance of critical applications Improve performance and reduce IT costs to benefit your bottom line Register for the Live Webcast Event Date: Thursday, May 20, 2010 Time: 10 a.m. PT/1 p.m. ET

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  • Big Data – Various Learning Resources – How to Start with Big Data? – Day 20 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned how to become a Data Scientist for Big Data. In this article we will go over various learning resources related to Big Data. In this series we have covered many of the most essential details about Big Data. At the beginning of this series, I have encouraged readers to send me questions. One of the most popular questions is - “I want to learn more about Big Data. Where can I learn it?” This is indeed a great question as there are plenty of resources out to learn about Big Data and it is indeed difficult to select on one resource to learn Big Data. Hence I decided to write here a few of the very important resources which are related to Big Data. Learn from Pluralsight Pluralsight is a global leader in high-quality online training for hardcore developers.  It has fantastic Big Data Courses and I started to learn about Big Data with the help of Pluralsight. Here are few of the courses which are directly related to Big Data. Big Data: The Big Picture Big Data Analytics with Tableau NoSQL: The Big Picture Understanding NoSQL Data Analysis Fundamentals with Tableau I encourage all of you start with this video course as they are fantastic fundamentals to learn Big Data. Learn from Apache Resources at Apache are single point the most authentic learning resources. If you want to learn fundamentals and go deep about every aspect of the Big Data, I believe you must understand various concepts in Apache’s library. I am pretty impressed with the documentation and I am personally referencing it every single day when I work with Big Data. I strongly encourage all of you to bookmark following all the links for authentic big data learning. Haddop - The Apache Hadoop® project develops open-source software for reliable, scalable, distributed computing. Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which include support for Hadoop HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heat maps and ability to view MapReduce, Pig and Hive applications visually along with features to diagnose their performance characteristics in a user-friendly manner. Avro: A data serialization system. Cassandra: A scalable multi-master database with no single points of failure. Chukwa: A data collection system for managing large distributed systems. HBase: A scalable, distributed database that supports structured data storage for large tables. Hive: A data warehouse infrastructure that provides data summarization and ad hoc querying. Mahout: A Scalable machine learning and data mining library. Pig: A high-level data-flow language and execution framework for parallel computation. ZooKeeper: A high-performance coordination service for distributed applications. Learn from Vendors One of the biggest issues with about learning Big Data is setting up the environment. Every Big Data vendor has different environment request and there are lots of things require to set up Big Data framework. Many of the users do not start with Big Data as they are afraid about the resources required to set up framework as well as a time commitment. Here Hortonworks have created fantastic learning environment. They have created Sandbox with everything one person needs to learn Big Data and also have provided excellent tutoring along with it. Sandbox comes with a dozen hands-on tutorial that will guide you through the basics of Hadoop as well it contains the Hortonworks Data Platform. I think Hortonworks did a fantastic job building this Sandbox and Tutorial. Though there are plenty of different Big Data Vendors I have decided to list only Hortonworks due to their unique setup. Please leave a comment if there are any other such platform to learn Big Data. I will include them over here as well. Learn from Books There are indeed few good books out there which one can refer to learn Big Data. Here are few good books which I have read. I will update the list as I will learn more. Ethics of Big Data Balancing Risk and Innovation Big Data for Dummies Head First Data Analysis: A Learner’s Guide to Big Numbers, Statistics, and Good Decisions If you search on Amazon there are millions of the books but I think above three books are a great set of books and it will give you great ideas about Big Data. Once you go through above books, you will have a clear idea about what is the next step you should follow in this series. You will be capable enough to make the right decision for yourself. Tomorrow In tomorrow’s blog post we will wrap up this series of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Data Storage Options

    - by Kenneth
    When I was working as a website designer/engineer I primarily used databases for storage of much of my dynamic data. It was very easy and convenient to use this method and seemed like a standard practice from my research on the matter. I'm now working on shifting away from websites and into desktop applications. What are the best practices for data storage for desktop applications? I ask because I have noticed that most programs I use on a personal level don't appear to use a database for data storage unless its embedded in the program. (I'm not thinking of an application like a word processor where it makes sense to have data stored in individual files as defined by the user. Rather I'm thinking of something more along the lines of a calendar application which would need to store dates and event info and such where accessing that information would be much easier if stored in a database... at least as far as my experience would indicate.) Thanks for the input!

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  • IOUG Webcast Series on Identity Management

    - by Tanu Sood
    Identity Management for Business Empowerment Identity Management has gone from the realm of IT tools to being a business solution. Security and Identity Management offer confidence in doing secure and compliant business. But more than that, Identity Management today contributes to business growth with secure social, cloud, mobile and internal & external ecosystem enablement. Cloud computing has heightened the interest in user access security, mobile computing brings access to information beyond the enterprise and a bring your own device culture in-house, social media has added a new dimension to user identity and increasing security compliance pressure has made organizations rethink their roles and entitlements strategy. To discuss the industry trends, maturity and framework for security, compliance and business empowerment with identity management, Oracle is proud to collaborate with IOUG to launch a series of live webcasts. Covering a span of topics from identity platform to entitlements managements, privilege access management and cloud, mobile and social security, these webcasts will provide direct access to subject matter experts and technology specialists. Hear first-hand about best practices, a pragmatic approach to security implementation, customer success stories and more. Register today for the individual webcasts or the series. And just a reminder that the conversation starts at COLLABORATE 12 in Las Vegas from April 22nd – 26th. In addition to our conference sessions, as an added value this year, we are offering a half-day deep dive session on Oracle Identity Management: Building a Security and Compliance Framework for Oracle Systems. The session is scheduled for Sunday, April 22nd from 9 am to 3 pm and will cover relevant topics such as: • A Primer on Identity Management • Security and Compliance with Oracle Identity Management • Security for Oracle Applications, Fusion Applications• Managing Identities in The Cloud and Mobile World • Best Practices: Building an Identity Roadmap and Getting Started To get a head start on your compliance and security program, pre-register for this session today.

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  • How Do You Actually Model Data?

    Since the 1970’s Developers, Analysts and DBAs have been able to represent concepts and relations in the form of data through the use of generic symbols.  But what is data modeling?  The first time I actually heard this term I could not understand why anyone would want to display a computer on a fashion show runway. Hey, what do you expect? At that time I was a freshman in community college, and obviously this was a long time ago.  I have since had the chance to learn what data modeling truly is through using it. Data modeling is a process of breaking down information and/or requirements in to common categories called objects. Once objects start being defined then relationships start to form based on dependencies found amongst other existing objects.  Currently, there are several tools on the market that help data designer actually map out objects and their relationships through the use of symbols and lines.  These diagrams allow for designs to be review from several perspectives so that designers can ensure that they have the optimal data design for their project and that the design is flexible enough to allow for potential changes and/or extension in the future. Additionally these basic models can always be further refined to show different levels of details depending on the target audience through the use of three different types of models. Conceptual Data Model(CDM)Conceptual Data Models include all key entities and relationships giving a viewer a high level understanding of attributes. Conceptual data model are created by gathering and analyzing information from various sources pertaining to a project during the typical planning phase of a project. Logical Data Model (LDM)Logical Data Models are conceptual data models that have been expanded to include implementation details pertaining to the data that it will store. Additionally, this model typically represents an origination’s business requirements and business rules by defining various attribute data types and relationships regarding each entity. This additional information can be directly translated to the Physical Data Model which reduces the actual time need to implement it. Physical Data Model(PDMs)Physical Data Model are transformed Logical Data Models that include the necessary tables, columns, relationships, database properties for the creation of a database. This model also allows for considerations regarding performance, indexing and denormalization that are applied through database rules, data integrity. Further expanding on why we actually use models in modern application/database development can be seen in the benefits that data modeling provides for data modelers and projects themselves, Benefits of Data Modeling according to Applied Information Science Abstraction that allows data designers remove concepts and ideas form hard facts in the form of data. This gives the data designers the ability to express general concepts and/or ideas in a generic form through the use of symbols to represent data items and the relationships between the items. Transparency through the use of data models allows complex ideas to be translated in to simple symbols so that the concept can be understood by all viewpoints and limits the amount of confusion and misunderstanding. Effectiveness in regards to tuning a model for acceptable performance while maintaining affordable operational costs. In addition it allows systems to be built on a solid foundation in terms of data. I shudder at the thought of a world without data modeling, think about it? Data is everywhere in our lives. Data modeling allows for optimizing a design for performance and the reduction of duplication. If one was to design a database without data modeling then I would think that the first things to get impacted would be database performance due to poorly designed database and there would be greater chances of unnecessary data duplication that would also play in to the excessive query times because unneeded records would need to be processed. You could say that a data designer designing a database is like a box of chocolates. You will never know what kind of database you will get until after it is built.

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  • Which management tools would you recommend for software development?

    - by Robert Schneider
    What would you generally recommend for software developement? Which combination do you use or would you recommend? I assume that tools are needed for Version control, issues/bugs/task, Release management, Requirement management. Tools for Test management and Project management should be accounted for as well, I guess. Did I forget anything? Maybe tools for Continous Integration. I'm not interested in a halfway combination of one ore two tools like a Subversion + Bugzilla (I know they are good but for a company they might not be sufficient). And also tools like make/ant shouldn't be taken into account. I'd like to know a combination that covers all what is important for professional software development. However, it could be a single tool of course if it covers all the management issues. What do you think would be a good combination? I assume a combination should be regardes as good if the tools itself are good but also if they have good integrations. Udate: Something like Ant is just a script not a management tool. Okay: we do already have Perforce. But this is somehow generic. We have different projects that uses C-, VS/.NET-, Python-, PHP and we will be starting new projects in Java. Plenty of languages and frameworks, though some are going to be legacy. So the tools should be generic. Obviously we don't want to use Bugzilla for one project and Jira for another. The management tools have to be generic. Further thoughts: Think of sourceforge: For each project they offer a version control system, an issue tracker, a wiki, ... totally independent of what the project is about. Those are some management tools that a project usually needs. I would say that most companies need such a set of tools. But I could imagine, if a company has several projects, they need further tools too: for Project management, Quality management, ... And I could imagine there are some recommendable combinations of tools. A not really apropiate comparison to this could be LAMP (Linux, Apache, MySQL, PHP). A combination of software that has evolved since they work very well together, and are pretty useful for a lot of applications (or rather web sites). So maybe there are some recommendable management tool combinations that also fit toghether like LAMP. The integration is important. Thank you for the incredibly fast and helpful responses!!!

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  • Data Structures usage and motivational aspects

    - by Aubergine
    For long student life I was always wondering why there are so many of them yet there seems to be lack of usage at all in many of them. The opinion didn't really change when I got a job. We have brilliant books on what they are and their complexities, but I never encounter resources which would actually give a good hint of practical usage. I perfectly understand that I have to look at problem , analyse required operations, look for data structure that does them efficiently. However in practice I never do that, not because of human laziness syndrome, but because when it comes to work I acknowledge time priority over self-development. Over time I thought that when I would be better developer I will automatically use more of them - that didn't happen at all or maybe I just didn't. Then I found that the colleagues usually in the same plate as me - knowing more or less some three of data structures and being totally happy about it and refusing to discuss this matter further with me, coming back to conversations about 'cool new languages' 'libraries that do jobs for you' and the joy to work under scrumban etc. I am stuck with ArrayLists, Arrays and SortedMap , which no matter what I do always suffice or either I tweak them to be capable of fulfilling my task. Yes, it might be inefficient but do we really have to care if Intel increases performance over years no matter if we improve our skills? Does new Xeon or IBM machines really care what we use? What if I like build things, but I am not particularly excited whether it is n log(n) or just n? Over twenty years the processing power increased enormously, which gives us freedom of not being critical about which one to use? On top of that new more optimized languages appear which support multiple cores more efficiently. To be more specific: I would like to find motivational material on complex real areas/cases of possible effective usages of data structures. I would be really grateful if you would provide relevant resources. There is similar question ,but in the end the links again mostly describe or do dumb example(vehicles, students or holy grail quest - yes, very relevant) them and people keep referring to the "scenario decides the data structure to use". I want to know these complex scenarios to be able to identify similarities to my scenario and then use them. The complex scenarios where it really matters and not necessarily of quantitive nature. It seems that data structures only concern is efficiency and nothing else? There seems to be no particular convenience for developer in use one over another. (only when I found scientific resources on why exactly simple carbohydrates are evil I stopped eating sugar and candies completely replacing it with less harmful fruits - I hope you can see the analogy)

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  • SQL SERVER – Step by Step Guide to Beginning Data Quality Services in SQL Server 2012 – Introduction to DQS

    - by pinaldave
    Data Quality Services is a very important concept of SQL Server. I have recently started to explore the same and I am really learning some good concepts. Here are two very important blog posts which one should go over before continuing this blog post. Installing Data Quality Services (DQS) on SQL Server 2012 Connecting Error to Data Quality Services (DQS) on SQL Server 2012 This article is introduction to Data Quality Services for beginners. We will be using an Excel file Click on the image to enlarge the it. In the first article we learned to install DQS. In this article we will see how we can learn about building Knowledge Base and using it to help us identify the quality of the data as well help correct the bad quality of the data. Here are the two very important steps we will be learning in this tutorial. Building a New Knowledge Base  Creating a New Data Quality Project Let us start the building the Knowledge Base. Click on New Knowledge Base. In our project we will be using the Excel as a knowledge base. Here is the Excel which we will be using. There are two columns. One is Colors and another is Shade. They are independent columns and not related to each other. The point which I am trying to show is that in Column A there are unique data and in Column B there are duplicate records. Clicking on New Knowledge Base will bring up the following screen. Enter the name of the new knowledge base. Clicking NEXT will bring up following screen where it will allow to select the EXCE file and it will also let users select the source column. I have selected Colors and Shade both as a source column. Creating a domain is very important. Here you can create a unique domain or domain which is compositely build from Colors and Shade. As this is the first example, I will create unique domain – for Colors I will create domain Colors and for Shade I will create domain Shade. Here is the screen which will demonstrate how the screen will look after creating domains. Clicking NEXT it will bring you to following screen where you can do the data discovery. Clicking on the START will start the processing of the source data provided. Pre-processed data will show various information related to the source data. In our case it shows that Colors column have unique data whereas Shade have non-unique data and unique data rows are only two. In the next screen you can actually add more rows as well see the frequency of the data as the values are listed unique. Clicking next will publish the knowledge base which is just created. Now the knowledge base is created. We will try to take any random data and attempt to do DQS implementation over it. I am using another excel sheet here for simplicity purpose. In reality you can easily use SQL Server table for the same. Click on New Data Quality Project to see start DQS Project. In the next screen it will ask which knowledge base to use. We will be using our Colors knowledge base which we have recently created. In the Colors knowledge base we had two columns – 1) Colors and 2) Shade. In our case we will be using both of the mappings here. User can select one or multiple column mapping over here. Now the most important phase of the complete project. Click on Start and it will make the cleaning process and shows various results. In our case there were two columns to be processed and it completed the task with necessary information. It demonstrated that in Colors columns it has not corrected any value by itself but in Shade value there is a suggestion it has. We can train the DQS to correct values but let us keep that subject for future blog posts. Now click next and keep the domain Colors selected left side. It will demonstrate that there are two incorrect columns which it needs to be corrected. Here is the place where once corrected value will be auto-corrected in future. I manually corrected the value here and clicked on Approve radio buttons. As soon as I click on Approve buttons the rows will be disappeared from this tab and will move to Corrected Tab. If I had rejected tab it would have moved the rows to Invalid tab as well. In this screen you can see how the corrected 2 rows are demonstrated. You can click on Correct tab and see previously validated 6 rows which passed the DQS process. Now let us click on the Shade domain on the left side of the screen. This domain shows very interesting details as there DQS system guessed the correct answer as Dark with the confidence level of 77%. It is quite a high confidence level and manual observation also demonstrate that Dark is the correct answer. I clicked on Approve and the row moved to corrected tab. On the next screen DQS shows the summary of all the activities. It also demonstrates how the correction of the quality of the data was performed. The user can explore their data to a SQL Server Table, CSV file or Excel. The user also has an option to either explore data and all the associated cleansing info or data only. I will select Data only for demonstration purpose. Clicking explore will generate the files. Let us open the generated file. It will look as following and it looks pretty complete and corrected. Well, we have successfully completed DQS Process. The process is indeed very easy. I suggest you try this out yourself and you will find it very easy to learn. In future we will go over advanced concepts. Are you using this feature on your production server? If yes, would you please leave a comment with your environment and business need. It will be indeed interesting to see where it is implemented. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: Data Quality Services, DQS

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  • Consolidate Data in Private Clouds, But Consider Security and Regulatory Issues

    - by Troy Kitch
    The January 13 webcast Security and Compliance for Private Cloud Consolidation will provide attendees with an overview of private cloud computing based on Oracle's Maximum Availability Architecture and how security and regulatory compliance affects implementations. Many organizations are taking advantage of Oracle's Maximum Availability Architecture to drive down the cost of IT by deploying private cloud computing environments that can support downtime and utilization spikes without idle redundancy. With two-thirds of sensitive and regulated data in organizations' databases private cloud database consolidation means organizations must be more concerned than ever about protecting their information and addressing new regulatory challenges. Join us for this webcast to learn about greater risks and increased threats to private cloud data and how Oracle Database Security Solutions can assist in securely consolidating data and meet compliance requirements. Register Now.

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