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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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• 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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• SQL SERVER – Data and Page Compressions – Data Storage and IO Improvement

  - by pinaldave

  The performance of SQL Server is primarily decided by the disk I/O efficiency. Improving I/O definitely improves the performance. SQL Server 2008 introduced Data and Backup compression features to improve the disk I/O. Here, I will explain Data compression. Data compression implies the reduction in the disk space reserved by data. Therefore, data compression can be [...]

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• Update/Insert With ADF Web Service Data Control

  - by shay.shmeltzer

  The Web service data control (WSDC) in ADF is a powerful feature that allows you to easily build a UI on top of WS interfaces exposed by other systems. However when you drag a WSDC to a page you usually get a set of output components where the data is shown. So how would you actually do an update operation on those values? The answer is that you need a call to another method in your WSDC that does the update - but what if you want to pass to it the actual values that you get from the get method you invoked before? Here is a demo showing how to do that: The two tricks that are shown here are: Changing the properties of items in the DC to be updateable - this gives you inputText fields instead of outputText fields. And passing the currentRow.dataProvider to the update method (and choosing the right iterator for this).

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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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• Using Maven for project distribution

  - by Thomas

  I have an Project that I distribute by sending out large packages. I'd like to know if there is a user friendly way of using Maven to distribute updates of the project? I'd need something like what is done for updating softwares like Firefox or Acrobat Reader. Check a respository, warn user of an update, download and reconfigure. All within a simple and friendly interface. Alternative open source java projects are welcome.

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• Power Distribution amongst connected nodes

  - by Perky

  In my game the map is represented by connected nodes, each node has a number of connected nodes. The nodes represent a system in which players can build structures and move units about. If you're familiar with Sins of a Solar Empire the game map is very similar. I want each node to be able to produce power and share it with all connected nodes. For example if A, B, C & D are all connected and produce 100 power units, then each system should have 400 power units available. If node B builds a structure that consumes 100 power units then A, B, C & D should then have 300 power units available. I've been working on this system all day and haven't been able to get it working quite the way I want. My current implementation is to first recurse through each nodes's connected node adding up the power, I keep a list of closed nodes so it doesn't loop, it's quite similar to A* actually. Pseudo code: All nodes start with the properties node.power = 0 node.basePower = 100 // could be different for each node. node.initialPower = node.basePower - function propagatePower( node, initialPower, closedNodes ) node.power += initialPower add( closedNodes, node ) connectedNodes = connected_nodes_except_from( closedNodes ) foreach node in connectedNodes do propagatePower( node, initialPower, closedNodes ) end end After this I iterate through all power consumers. foreach consumer in consumers do node = consumer.parentNode if node.power >= consumer.powerConsumption then consumer.powerConsumed += consumer.powerConsumption node.producedPower -= consumer.powerConsumption end end Then I adjust the initial power for the next propagation cycle. foreach node in nodes do node.initialPower = node.basePower - node.producedPower node.displayPower = node.power // for rendering the power. node.power = 0 end This seemed to work at first but then I came into a problem. Say two nodes A & B produce 100Pu each, it's shared so both A & B have 200Pu. I then make two structures that consume 80Pu each on A (160Pu). Then the nodes power is adjusted to basePower - producedPower (100-160 = -60). Nodes are propagated, both nodes now have 40Pu (A: -60 + B: 100 = 40). Which is correct because they started with 200Pu - 160Pu = 40Pu. However now node.power >= consumer.powerConsumption is false. Whats worse is it's false for any structure that uses more that 40Pu, so the whole system goes down. I could deduct from consumer.powerConsumption but what do I do if power is reduced elsewhere? I don't have the correct data to perform the necessary checks. It's late so I'm probably not thinking straight but I thought to ask on here to see if anyone has any other implementations, better or worse I'd be interested to know.

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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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• Single python file distribution: module or package?

  - by DanielSank

  Suppose I have a useful python function or class (or whatever) called useful_thing which exists in a single file. There are essentialy two ways to organize the source tree. The first way uses a single module: - setup.py - README.rst - ...etc... - foo.py where useful_thing is defined in foo.py. The second strategy is to make a package: - setup.py - README.rst - ...etc... - foo |-module.py |-__init__.py where useful_thing is defined in module.py. In the package case __init__.py would look like this from foo.module import useful_thing so that in both cases you can do from foo import useful_thing. Question: Which way is preferred, and why? EDIT: Since user gnat says this question is poorly formed, I'll add that the official python packaging tutorial does not seem to comment on which of the methods described above is the preferred one. I am explicitly not giving my personal list of pros and cons because I'm interested in whether there is a community preferred method, not generating a discussion of pros/cons :)

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• Oracle Java Cloud Service - Platform as a Service for Your Java Applications

  - by GeneEun

  Oracle Java Cloud Service is an enterprise grade Platform as a Service for developing, testing, and deploying business applications. For Java developers, Java Cloud Service provides the power, flexibility, and performance of a true Java EE container in the cloud. Java Cloud Service delivers one of the key advantages of the Java platform, the ability to “write once, run anywhere”. Because of the standards-based approach, there's no need to worry that applications you build and deploy are forever locked into the Oracle Cloud.  In fact, you can use Java Cloud Service just as you would an on-premise Java EE environment and deploy your Java applications on a Java Cloud Service instance as-is. Provisioning of Java Cloud Service instances is self-service and takes only minutes, making access to Java environments both quick and easy. Java Cloud Service instances are also automatically associated with Oracle Database Cloud Service instances, so there's no complex setup involved in order to get a complete application environment up and running.If you're attending Oracle OpenWorld in San Francisco this week, I'm sure you've seen that there are many sessions covering Oracle Cloud services, including Java Cloud Service. Each session will provide a wealth of information, so I highly recommend you consult your conference schedule and try to check them out. In the meantime, here's a short video about Java Cloud Service. Enjoy!

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• share distribution question

  - by facebook-100000781341887

  Hi, I just developed a facebook game(mifia like), but the graphic I make is not good, because it is reference with some existing photo, trace with AI, and coloring it. Therefore, I invite my friend to join me, he is a graphic designer, own a company with his friend (I know both of them), for the share, I expect at least 70% for me, and at most 30% for them (both of them want to join). Therefore, they give me a counter offer, 60% for me and 40% for them, of course, I feel their counter offer is unacceptable because they only build the image in part time, and all the other work just like coding, webhosting...etc, is what I do in full time. Why they said they worth 40% is that they will make a good graphic, they can provide a advertise channel(on local magazine), etc... Actually, I don't think the game need advertisement on local magazine because the game is not target for local... Please give me some comments on this issue(is the share fair? what is the importance of the image of the game, is it worth more than 30%), or can anyone share the experience on this. Thanks in advance.

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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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• 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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• What is a Data Warehouse?

  Typically Data Warehouses are considered to be non-volatile in comparison to traditional databasesdue to the fact that data within the warehouse does not change that often.  In addition, Data Warehouses typically represent data through the use of Multidimensional Conceptual Views that allow data to be extracted based on the view and the current position within the view. Common Data Warehouse Traits Relatively Non-volatile Data Supports Data Extraction and Analysis Optimized for Data Retrieval and Analysis Multidimensional Views of Data Flexible Reporting Multi User Support Generic Dimensionality Transparent Accessible Unlimited Dimensions of Data Unlimited Aggregation levels of Data Normally, Data Warehouses are much larger then there traditional database counterparts due to the fact that they store the basis data along with derived data via Multidimensional Conceptual Views. As companies store larger and larger amounts of data, they will need a way to effectively and accurately extract analysis information that can be used to aide in formulating current and future business decisions. This process can be done currently through data mining within a Data Warehouse. Data Warehouses provide access to data derived through complex analysis, knowledge discovery and decision making. Secondly, they support the demands for high performance in regards to analyzing an organization’s existing and current data. Data Warehouses provide support for an organization’s data and acquired business knowledge.  Within a Data Warehouse multiple types of operations/sub systems are supported. Common Data Warehouse Sub Systems Online Analytical Processing (OLAP) Decision –Support Systems (DSS) Online Transaction Processing (OLTP)

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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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• Java Cloud Service Integration using Web Service Data Control

  - by Jani Rautiainen

  Java Cloud Service (JCS) provides a platform to develop and deploy business applications in the cloud. In Fusion Applications Cloud deployments customers do not have the option to deploy custom applications developed with JDeveloper to ensure the integrity and supportability of the hosted application service. Instead the custom applications can be deployed to the JCS and integrated to the Fusion Application Cloud instance.This series of articles will go through the features of JCS, provide end-to-end examples on how to develop and deploy applications on JCS and how to integrate them with the Fusion Applications instance.In this article a custom application integrating with Fusion Application using Web Service Data Control will be implemented. v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";} Pre-requisites Access to Cloud instance In order to deploy the application access to a JCS instance is needed, a free trial JCS instance can be obtained from Oracle Cloud site. To register you will need a credit card even if the credit card will not be charged. To register simply click "Try it" and choose the "Java" option. The confirmation email will contain the connection details. See this video for example of the registration. Once the request is processed you will be assigned 2 service instances; Java and Database. Applications deployed to the JCS must use Oracle Database Cloud Service as their underlying database. So when JCS instance is created a database instance is associated with it using a JDBC data source. The cloud services can be monitored and managed through the web UI. For details refer to Getting Started with Oracle Cloud. JDeveloper JDeveloper contains Cloud specific features related to e.g. connection and deployment. To use these features download the JDeveloper from JDeveloper download site by clicking the “Download JDeveloper 11.1.1.7.1 for ADF deployment on Oracle Cloud” link, this version of JDeveloper will have the JCS integration features that will be used in this article. For versions that do not include the Cloud integration features the Oracle Java Cloud Service SDK or the JCS Java Console can be used for deployment. For details on installing and configuring the JDeveloper refer to the installation guide. For details on SDK refer to Using the Command-Line Interface to Monitor Oracle Java Cloud Service and Using the Command-Line Interface to Manage Oracle Java Cloud Service. Create Application In this example the “JcsWsDemo” application created in the “Java Cloud Service Integration using Web Service Proxy” article is used as the base. Create Web Service Data Control In this example we will use a Web Service Data Control to integrate with Credit Rule Service in Fusion Applications. The data control will be used to query data from Fusion Applications using a web service call and present the data in a table. To generate the data control choose the “Model” project and navigate to "New -> All Technologies -> Business Tier -> Data Controls -> Web Service Data Control" and enter following: Name: CreditRuleServiceDC URL: https://ic-[POD].oracleoutsourcing.com/icCnSetupCreditRulesPublicService/CreditRuleService?WSDL Service: {{http://xmlns.oracle.com/apps/incentiveCompensation/cn/creditSetup/creditRule/creditRuleService/}CreditRuleService On step 2 select the “findRule” operation: Skip step 3 and on step 4 define the credentials to access the service. Do note that in this example these credentials are only used if testing locally, for JCS deployment credentials need to be manually updated on the EAR file: Click “Finish” and the proxy generation is done. Creating UI In order to use the data control we will need to populate complex objects FindCriteria and FindControl. For simplicity in this example we will create logic in a managed bean that populates the objects. Open “JcsWsDemoBean.java” and add the following logic: Map findCriteria; Map findControl; public void setFindCriteria(Map findCriteria) { this.findCriteria = findCriteria; } public Map getFindCriteria() { findCriteria = new HashMap(); findCriteria.put("fetchSize",10); findCriteria.put("fetchStart",0); return findCriteria; } public void setFindControl(Map findControl) { this.findControl = findControl; } public Map getFindControl() { findControl = new HashMap(); return findControl; } Open “JcsWsDemo.jspx”, navigate to “Data Controls -> CreditRuleServiceDC -> findRule(Object, Object) -> result” and drag and drop the “result” node into the “af:form” element in the page: On the “Edit Table Columns” remove all columns except “RuleId” and “Name”: On the “Edit Action Binding” window displayed enter reference to the java class created above by selecting “#{JcsWsDemoBean.findCriteria}”: Also define the value for the “findControl” by selecting “#{JcsWsDemoBean.findControl}”. Deploy to JCS For WS DC the authentication details need to be updated on the connection details before deploying. Open “connections.xml” by navigating “Application Resources -> Descriptors -> ADF META-INF -> connections.xml”: Change the user name and password entry from: <soap username="transportUserName" password="transportPassword" To match the access details for the target environment. Follow the same steps as documented in previous article ”Java Cloud Service ADF Web Application”. Once deployed the application can be accessed with URL: https://java-[identity domain].java.[data center].oraclecloudapps.com/JcsWsDemo-ViewController-context-root/faces/JcsWsDemo.jspx When accessed the first 10 rules in the system are displayed: Summary In this article we learned how to integrate with Fusion Applications using a Web Service Data Control in JCS. In future articles various other integration techniques will be covered. 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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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• 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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• 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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• Validating Data Using Data Annotation Attributes in ASP.NET MVC

  - by bipinjoshi

  The data entered by the end user in various form fields must be validated before it is saved in the database. Developers often use validation HTML helpers provided by ASP.NET MVC to perform the input validations. Additionally, you can also use data annotation attributes from the System.ComponentModel.DataAnnotations namespace to perform validations at the model level. Data annotation attributes are attached to the properties of the model class and enforce some validation criteria. They are capable of performing validation on the server side as well as on the client side. This article discusses the basics of using these attributes in an ASP.NET MVC application.http://www.bipinjoshi.net/articles/0a53f05f-b58c-47b1-a544-f032f5cfca58.aspx       

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• Running ODI 11gR1 Standalone Agent as a Windows Service

  - by fx.nicolas

  ODI 11gR1 introduces the capability to use OPMN to start and protect agent processes as services. Setting up the OPMN agent is covered in the following post and extensively in the ODI Installation Guide. Unfortunately, OPMN is not installed along with ODI, and ODI 10g users who are really at ease with the old Java Wrapper are a little bit puzzled by OPMN, and ask: "How can I simply set up the agent as a service?". Well... although the Tanuki Service Wrapper is no longer available for free, and the agentservice.bat script lost, you can switch to another service wrapper for the same result. For example, Yet Another Java Service Wrapper (YAJSW) is a good candidate. To configure a standalone agent with YAJSW: download YAJSW Uncompress the zip to a folder (called %YAJSW% in this example) Configure, start and test your standalone agent. Make sure that this agent is loaded with all the required libraries and drivers, as the service will not load dynamically the drivers added subsequently in the /drivers directory. Retrieve the PID of the agent process: Open Task Manager. Select View Select Columns Select the PID (Process Identifier) column, then click OK In the list of processes, find the java.exe process corresponding to your agent, and note its PID. Open a command line prompt in %YAJSW%/bat and run: genConfig.bat <your_pid> This command generates a wrapper configuration file for the agent. This file is called %YAJSW%/conf/wrapper.conf. Stop your agent. Edit the wrapper.conf file and modify the configuration of your service. For example, modify the display name and description of the service as shown in the example below. Important: Make sure to escape the commas in the ODI encoded passwords with a backslash! In the example below, the ODI_SUPERVISOR_ENCODED_PASS contained a comma character which had to be prefixed with a backslash. # Title to use when running as a console wrapper.console.title=\"AGENT\" #******************************************************************** # Wrapper Windows Service and Posix Daemon Properties #******************************************************************** # Name of the service wrapper.ntservice.name=AGENT_113 # Display name of the service wrapper.ntservice.displayname=ODI Agent # Description of the service wrapper.ntservice.description=Oracle Data Integrator Agent 11gR3 (11.1.1.3.0) ... # Escape the comma in the password with a backslash. wrapper.app.parameter.7 = -ODI_SUPERVISOR_ENCODED_PASS=fJya.vR5kvNcu9TtV\,jVZEt Execute your wrapped agent as console by calling in the command line prompt: runConsole.bat Check that your agent is running, and test it again.This command starts the agent with the configuration but does not install it yet as a service. To Install the agent as service call installService.bat From that point, you can view, start and stop the agent via the windows services. Et voilà ! Two final notes: - To modify the agent configuration, you must uninstall/reinstall the service. For this purpose, run the uninstallService.bat to uninstall it and play again the process above. - To be able to uninstall the agent service, you should keep a backup of the wrapper.conf file. This is particularly important when starting several services with the wrapper.

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