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  • Core data migration failing with "Can't find model for source store" but managedObjectModel for source is present

    - by Ira Cooke
    I have a cocoa application using core-data, which is now at the 4th version of its managed object model. My managed object model contains abstract entities but so far I have managed to get migration working by creating appropriate mapping models and creating my persistent store using addPersistentStoreWithType:configuration:options:error and with the NSMigratePersistentStoresAutomaticallyOption set to YES. NSDictionary *optionsDictionary = [NSDictionary dictionaryWithObject:[NSNumber numberWithBool:YES] forKey:NSMigratePersistentStoresAutomaticallyOption]; NSURL *url = [NSURL fileURLWithPath: [applicationSupportFolder stringByAppendingPathComponent: @"MyApp.xml"]]; NSError *error=nil; [theCoordinator addPersistentStoreWithType:NSXMLStoreType configuration:nil URL:url options:optionsDictionary error:&error] This works fine when I migrate from model version 3 to 4, which is a migration that involves adding attributes to several entities. Now when I try to add a new model version (version 5), the call to addPersistentStoreWithType returns nil and the error remains empty. The migration from 4 to 5 involves adding a single attribute. I am struggling to debug the problem and have checked all the following; The source database is in fact at version 4 and the persistentStoreCoordinator's managed object model is at version 5. The 4-5 mapping model as well as managed object models for versions 4 and 5 are present in the resources folder of my built application. I've tried various model upgrade paths. Strangely I find that upgrading from an early version 3 - 5 works .. but upgrading from 4 - 5 fails. I've tried adding a custom entity migration policy for migration of the entity whose attributes are changing ... in this case I overrode the method beginEntityMapping:manager:error: . Interestingly this method does get called when migration works (ie when I migrate from 3 to 4, or from 3 to 5 ), but it does not get called in the case that fails ( 4 to 5 ). I'm pretty much at a loss as to where to proceed. Any ideas to help debug this problem would be much appreciated.

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  • Handling Denormalized Schema with Eclipselink

    - by iamrohitbanga
    Hello All I have a denormalized table containing employee information. The fields are employee id, name and department name. The primary key is a composite one consisting of all three fields. An employee can belong to multiple departments. I want to read/write the objects in the table using the Eclipselink Dynamic Persistence API (which is infact a wrapper on top of JPA descriptors etc.). Example Data: 1 e1 dep1 2 e1 dep2 3 e2 dep1 4 e2 dep3 5 e3 dep1 5 e3 dep2 5 e3 dep3 A normal ReadAllQuery (select query) on the table returns a DynamicEntity corresponding to each row in the table. However I want to club all entities based on the emp id and return all the departments he belongs to as a list. I can merge the entities after retrieving them but if I can use some Eclipselink feature out of the box then it would be better. One way to do the read is the following: I create two dynamic types corresponding to employee: Having id,name as the primary key Having id, department as the primary key, I create a OneToManyMapping from the first type to the second one. Then when I query the first type it does return the departments to which employee belongs as a list of DynamicEntity of the second type. This satisfies the read scenario. Is there a better way of doing this? Is this inherently supported by Eclipselink or JPA? I cannot get the same dynamic type configuration working for the write scenario. This is because when I write the changes using the writeObject method of UnitOfWork, it generates insert queries which enter the following entries in the table id name department 102 emp_102 102 st 102 dep_102 102 dep_102 102 dep_102 instead of: id name department 102 emp_102 st 102 emp_102 dep_102 102 emp_102 dep_102 102 emp_102 dep_102 Is there any way I can get write to work with this schema using eclipselink? I want to avoid doing the heavy lifting of merging the rows for such a denormalized schema or generating each row before doing a write. Is there no clean way of doing this using Eclipselink or JPA? Thanks in Advance.

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  • Handling primary key duplicates in a data warehouse load

    - by Meff
    I'm currently building an ETL system to load a data warehouse from a transactional system. The grain of my fact table is the transaction level. In order to ensure I don't load duplicate rows I've put a primary key on the fact table, which is the transaction ID. I've encountered a problem with transactions being reversed - In the transactional database this is done via a status, which I pick up and I can work out if the transaction is being done, or rolled back so I can load a reversal row in the warehouse. However, the reversal row will have the same transaction ID and so I get a primary key violation. I've solved this for now by negating the primary key, so transaction ID 1 would be a payment, and transaction ID -1 (In the warehouse only) would be the reversal. I have considered an alternative of generating a BIT column, where 0 is normal and 1 is reversal, then making the PK the transaction ID and the BIT column. My question is, is this a good practice, and has anyone else encountered anything like this? For reference, this is a payment processing system, so values will not be modified, so there will only ever be transactions and reversals.

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  • Bridging the Gap in Cloud, Big Data, and Real-time

    - by Dain C. Hansen
    Normal 0 false false false EN-US X-NONE X-NONE /* 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:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} With all the buzz of around big data and cloud computing, it is easy to overlook one of your most precious commodities—your data. Today’s businesses cannot stand still when it comes to data. Market success now depends on speed, volume, complexity, and keeping pace with the latest data integration breakthroughs. Are you up to speed with big data, cloud integration, real-time analytics? Join us in this three part blog series where we’ll look at each component in more detail. Meet us online on October 24th where we’ll take your questions about what issues you are facing in this brave new world of integration. Let’s start first with Cloud. What happens with your data when you decide to implement a private cloud architecture? Or public cloud? Data integration solutions play a vital role migrating data simply, efficiently, and reliably to the cloud; they are a necessary ingredient of any platform as a service strategy because they support cloud deployments with data-layer application integration between on-premise and cloud environments of all kinds. For private cloud architectures, consolidation of your databases and data stores is an important step to take to be able to receive the full benefits of cloud computing. Private cloud integration requires bidirectional replication between heterogeneous systems to allow you to perform data consolidation without interrupting your business operations. In addition, integrating data requires bulk load and transformation into and out of your private cloud is a crucial step for those companies moving to private cloud. In addition, the need for managing data services as part of SOA/BPM solutions that enable agile application delivery and help build shared data services for organizations. But what about public Cloud? If you have moved your data to a public cloud application, you may also need to connect your on-premise enterprise systems and the cloud environment by moving data in bulk or as real-time transactions across geographies. For public and private cloud architectures both, Oracle offers a complete and extensible set of integration options that span not only data integration but also service and process integration, security, and management. For those companies investing in Oracle Cloud, you can move your data through Oracle SOA Suite using REST APIs to Oracle Messaging Cloud Service —a new service that lets applications deployed in Oracle Cloud securely and reliably communicate over Java Messaging Service . As an example of loading and transforming data into other public clouds, Oracle Data Integrator supports a knowledge module for Salesforce.com—now available on AppExchange. Other third-party knowledge modules are being developed by customers and partners every day. To learn more about how to leverage Oracle’s Data Integration products for Cloud, join us live: Data Integration Breakthroughs Webcast on October 24th 10 AM PST.

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  • How to Secure a Data Role by Multiple Business Units

    - by Elie Wazen
    In this post we will see how a Role can be data secured by multiple Business Units (BUs).  Separate Data Roles are generally created for each BU if a corresponding data template generates roles on the basis of the BU dimension. The advantage of creating a policy with a rule that includes multiple BUs is that while mapping these roles in HCM Role Provisioning Rules, fewer number of entires need to be made. This could facilitate maintenance for enterprises with a large number of Business Units. Note: The example below applies as well if the securing entity is Inventory Organization. Let us take for example the case of a user provisioned with the "Accounts Payable Manager - Vision Operations" Data Role in Fusion Applications. This user will be able to access Invoices in Vision Operations but will not be able to see Invoices in Vision Germany. Figure 1. A User with a Data Role restricting them to Data from BU: Vision Operations With the role granted above, this is what the user will see when they attempt to select Business Units while searching for AP Invoices. Figure 2.The List Of Values of Business Units is limited to single one. This is the effect of the Data Role granted to that user as can be seen in Figure 1 In order to create a data role that secures by multiple BUs,  we need to start by creating a condition that groups those Business Units we want to include in that data role. This is accomplished by creating a new condition against the BU View .  That Condition will later be used to create a data policy for our newly created Role.  The BU View is a Database resource and  is accessed from APM as seen in the search below Figure 3.Viewing a Database Resource in APM The next step is create a new condition,  in which we define a sql predicate that includes 2 BUs ( The ids below refer to Vision Operations and Vision Germany).  At this point we have simply created a standalone condition.  We have not used this condition yet, and security is therefore not affected. Figure 4. Custom Role that inherits the Purchase Order Overview Duty We are now ready to create our Data Policy.  in APM, we search for our newly Created Role and Navigate to “Find Global Policies”.  we query the Role we want to secure and navigate to view its global policies. Figure 5. The Job Role we plan on securing We can see that the role was not defined with a Data Policy . So will create one that uses the condition we created earlier.   Figure 6. Creating a New Data Policy In the General Information tab, we have to specify the DB Resource that the Security Policy applies to:  In our case this is the BU View Figure 7. Data Policy Definition - Selection of the DB Resource we will secure by In the Rules Tab, we  make the rule applicable to multiple values of the DB Resource we selected in the previous tab.  This is where we associate the condition we created against the BU view to this data policy by entering the Condition name in the Condition field Figure 8. Data Policy Rule The last step of Defining the Data Policy, consists of  explicitly selecting  the Actions that are goverened by this Data Policy.  In this case for example we select the Actions displayed below in the right pane. Once the record is saved , we are ready to use our newly secured Data Role. Figure 9. Data Policy Actions We can now see a new Data Policy associated with our Role.  Figure 10. Role is now secured by a Data Policy We now Assign that new Role to the User.  Of course this does not have to be done in OIM and can be done using a Provisioning Rule in HCM. Figure 11. Role assigned to the User who previously was granted the Vision Ops secured role. Once that user accesses the Invoices Workarea this is what they see: In the image below the LOV of Business Unit returns the two values defined in our data policy namely: Vision Operations and Vision Germany Figure 12. The List Of Values of Business Units now includes the two we included in our data policy. This is the effect of the data role granted to that user as can be seen in Figure 11

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  • First Day of Data Integration Track at Oracle OpenWorld 2012

    - by Irem Radzik
    OpenWorld started full speed for us today with a great set of sessions in the Data Integration track. After the exciting keynote session on Oracle Database 12c in the morning; Brad Adelberg, VP of Development for Data Integration products, presented Oracle’s data integration product strategy. His session highlighted the new requirements for data integration to achieve pervasive and continuous access to trusted data. The new requirements and product focus areas presented in this session are: Provide access to any data at any source On premise or on cloud Enable zero downtime operations and maximum performance Leverage real-time data for accurate business insights And ensure high quality data is used across the enterprise During the session Brad walked over how Oracle’s data integration products, Oracle Data Integrator, Oracle GoldenGate, Oracle Enterprise Data Quality, and Oracle Data Service Integrator, deliver on these requirements and how recent product releases build on this strategy. Soon after Brad’s session we heard from a panel of Oracle GoldenGate customers, St. Jude Medical, Equifax, and Bank of America, how they achieved zero downtime operations using Oracle GoldenGate. The panel presented different use cases of GoldenGate, from Active-Active replication to offloading reporting. Especially St. Jude Medical’s implementation, which involves the alert management system for patients that use their pacemakers, reminded me in some cases downtime of mission-critical systems can be a matter of life or death. It is very comforting to hear that GoldenGate delivers highly-reliable continuous availability for life-saving medical systems. In the afternoon, Nick Wagner from the Product Management team and I followed the customer panel with the review of Oracle GoldenGate 11gR2’s New Features.  Many questions we received from audience were about GoldenGate’s new Integrated Capture for Oracle Database and the enhanced Conflict Management features, as well as how GoldenGate compares to Oracle Streams. In addition to giving details on GoldenGate’s unique capability to capture changed data with a direct integration to the Oracle DBMS engine, we reminded the audience that enhancements to Oracle GoldenGate will continue, while Streams will be primarily maintained. Last but not least, Tim Garrod and Ryan Fonnett from Raymond James presented a unified real-time data integration solution using Oracle Data Integrator and GoldenGate for their operational data store (ODS). The ODS supports application services across the enterprise and providing timely data is a critical requirement. In this solution, Oracle GoldenGate does the log-based change data capture for Oracle Data Integrator’s near real-time data integration between heterogeneous systems. As Raymond James’ ODS supports mission-critical services for their advisors, the project team had to set up this integration environment to be highly available. During the session, Ryan and Tim explained how they use ODI to enable automated process execution and “always-on” integration processes. Their presentation included 2 demonstrations that focused on CDC patterns deployed with ODI and the automated multi-instance execution and monitoring. We are very grateful to Tim and Ryan for their very-well prepared presentation at OpenWorld this year. Day 2 (Tuesday) will be also a busy day in our track. In addition to the Fusion Middleware Innovation Awards ceremony at 11:45am at Moscone West 3001, we have the following DI sessions Real-World Operational Reporting Customer Panel 11:45am Moscone West- 3005 Oracle Data Integrator Product Update and Future Strategy 1:15pm Moscone West- 3005 High-volume OLTP with Oracle GoldenGate: Best Practices from Comcast 1:15pm Moscone West- 3005 Everything You need to Know about Monitoring Oracle GoldenGate 5pm Moscone West-3005 If you are at OpenWorld please join us in these sessions. For a full review of data integration track at OpenWorld please see our Focus-On document.

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  • ADO.NET Data Services Entity Framework request error when property setter is internal

    - by Jim Straatman
    I receive an error message when exposing an ADO.NET Data Service using an Entity Framework data model that contains an entity (called "Case") with an internal setter on a property. If I modify the setter to be public (using the entity designer), the data services works fine. I don’t need the entity "Case" exposed in the data service, so I tried to limit which entities are exposed using SetEntitySetAccessRule. This didn’t work, and service end point fails with the same error. public static void InitializeService(IDataServiceConfiguration config) { config.SetEntitySetAccessRule("User", EntitySetRights.AllRead); } The error message is reported in a browser when the .svc endpoint is called. It is very generic, and reads “Request Error. The server encountered an error processing the request. See server logs for more details.” Unfortunately, there are no entries in the System and Application event logs. I found this stackoverflow question that shows how to configure tracing on the service. After doing so, the following NullReferenceExceptoin error was reported in the trace log. Does anyone know how to avoid this exception when including an entity with an internal setter? Blockquote 131076 3 0 2 MOTOJIM http://msdn.microsoft.com/en-US/library/System.ServiceModel.Diagnostics.TraceHandledException.aspx Handling an exception. 685a2910-19-128703978432492675 System.NullReferenceException, mscorlib, Version=2.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089 Object reference not set to an instance of an object. at System.Data.Services.Providers.ObjectContextServiceProvider.PopulateMemberMetadata(ResourceType resourceType, MetadataWorkspace workspace, IDictionary2 entitySets, IDictionary2 knownTypes) at System.Data.Services.Providers.ObjectContextServiceProvider.PopulateMetadata(IDictionary2 knownTypes, IDictionary2 entitySets) at System.Data.Services.Providers.BaseServiceProvider.PopulateMetadata() at System.Data.Services.DataService1.CreateProvider(Type dataServiceType, Object dataSourceInstance, DataServiceConfiguration&amp; configuration) at System.Data.Services.DataService1.EnsureProviderAndConfigForRequest() at System.Data.Services.DataService1.ProcessRequestForMessage(Stream messageBody) at SyncInvokeProcessRequestForMessage(Object , Object[] , Object[] ) at System.ServiceModel.Dispatcher.SyncMethodInvoker.Invoke(Object instance, Object[] inputs, Object[]&amp; outputs) at System.ServiceModel.Dispatcher.DispatchOperationRuntime.InvokeBegin(MessageRpc&amp; rpc) at System.ServiceModel.Dispatcher.ImmutableDispatchRuntime.ProcessMessage5(MessageRpc&amp; rpc) at System.ServiceModel.Dispatcher.ImmutableDispatchRuntime.ProcessMessage4(MessageRpc&amp; rpc) at System.ServiceModel.Dispatcher.ImmutableDispatchRuntime.ProcessMessage3(MessageRpc&amp; rpc) at System.ServiceModel.Dispatcher.ImmutableDispatchRuntime.ProcessMessage2(MessageRpc&amp; rpc) at System.ServiceModel.Dispatcher.ImmutableDispatchRuntime.ProcessMessage1(MessageRpc&amp; rpc) at System.ServiceModel.Dispatcher.MessageRpc.Process(Boolean isOperationContextSet) </StackTrace> <ExceptionString>System.NullReferenceException: Object reference not set to an instance of an object. at System.Data.Services.Providers.ObjectContextServiceProvider.PopulateMemberMetadata(ResourceType resourceType, MetadataWorkspace workspace, IDictionary2 entitySets, IDictionary2 knownTypes) at System.Data.Services.Providers.ObjectContextServiceProvider.PopulateMetadata(IDictionary2 knownTypes, IDictionary2 entitySets) at System.Data.Services.Providers.BaseServiceProvider.P

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  • How can I switch off exception handling in MSVC?

    - by Tamara
    Does anybody know how to switch off exception handling option in MSVC? I tried to set the option 'Enable C++ exceptions' to 'NO' and I got warning: warning C4530: C++ exception handler used, but unwind semantics are not enabled. Specify /EHsc I would like to switch off the exception handler, too, but I don't know how. In my application I basically need more speer than stability, therefore I chose switching off the exception handling. I do not have any try/catch blocks, but I do use STL. When I switch the option 'Enable C++ exceptions' to 'NO' is there any way how to get rid of those warnings?

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  • Reading data file into an array C#

    - by Whitey
    I have an array like this: int[,] iMap = new int[iMapHeight, iMapWidth] { { 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2}, { 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 1, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0}, { 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, }; And I want the same thing coming from a data file. The file should be structured like this preferably: 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 0000000000000000000000000 Which will ultimately make an array like above with all values set to 0. What would be the best way to do this? Would I read one line from the file and then split each characters separately and transfer it to the new array? Or perhaps read a line, and then read each character and put that in the array? Thanks for any help.

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  • Import exponetial fixed width format data into Excel

    - by Tom Daniel
    I've received a bunch of text data files consiting of Lots of records (30K/file) of 3 fields each of 5-place numbers in exponential format: s0.nnnnnEsee (where s is +/-, n is a digit and ee is the exponent (always 2 digit). When I open the file in Notepad, the format is perfectly uniform throughout each file, but when I import it to Excel using Data|Import|Fixed Width, many of the data values get messed up, no matter what format (text, exponential, various custom tries) I assign to the cells. Looking at the Notepad version, it appears that leading + signs were replaced with a space in the data file, but the sign of the exponential is always there. This means that some fields begin with a space, and this appears to confuse the Excel import routine. I get the same result in Excel 2003 and 2007. I'm sure there's a straightforward solution (hopefully without a messy VBA routine), but I can't figure out what to try next. :-) To clarify (hopefully), here are some input records and the corresponding text input to Excel: Notepad Excel -0.11311E+01 0.10431E-04 0.27018E-03 -0.11311E 1.0431E-05 2.7018E-04 0.19608E+00-0.81414E-02-0.89553E-02 0.19608E -8.1414E-03 8.9553E-03 etc. Whoopee! Solved my own problem - in the spirit of Jeopardy, now that I've begun the question, here's the answer - Use a different "File Origin" - several other than the default "Unicode UTF..." work fine! What a pain. Hope this helps somebody else avoid a few unpleasant hours! Aloha from Kona, Tom

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  • Recover data from quick formatted DVD-R

    - by Andrii Kalytiiuk
    I need to recover data from quick-formatted DVD-R. Please advise a free of charge option (cheap commercial tools will be ok either). Disk was partially recorded with Windows built in disk recorder and recording most likely was not complete. Afterwards I have inserted partially recorded DVD again and on Windows recorder's message box 'How to use this disk?' selected - 'use for CD/DVD player' and data was completely lost - as new recording session was started. Files of photos were recorded on disk. What I have tried so far: DiskInternals CD-DVD recovery - sees 5 jpg files but can't show preview. Tool is commercial - trial version does not allow to recover files. CDCheck - doesn't see any files and reports errors at attempt to scand DVD CD Recovery Toolbox Free - does not even recognize DVD drive ISO Buster - recognizes two files - one MP3 file for 99% of recorded size and one ARC file for about 100 KB MiniTool Power Data Recovery - Free Edition - does not see any files on DVD Stellar Phoenix CD DVD Data Recovery - does not see any files BinaryBiz Virtual Lab - sees DVD disk but needs license to browse content Please advise how is it possible to recover files from DVD.

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  • Exception Handling And Other Contentious Political Topics

    - by Justin Jones
    So about three years ago, around the time of my last blog post, I promised a friend I would write this post. Keeping promises is a good thing, and this is my first step towards easing back into regular blogging. I fully expect him to return from Pennsylvania to buy me a beer over this. However, it’s been an… ahem… eventful three years or so, and blogging, unfortunately, got pushed to the back burner on my priority list, along with a few other career minded activities. Now that the personal drama of the past three years is more or less resolved, it’s time to put a few things back on the front burner. What I consider to be proper exception handling practices is relatively well known these days. There are plenty of blog posts out there already on this topic which more or less echo my opinions on this topic. I’ll try to include a few links at the bottom of the post. Several years ago I had an argument with a co-worker who posited that exceptions should be caught at every level and logged. This might seem like sanity on the surface, but the resulting error log looked something like this: Error: System.SomeException Followed by small stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace. Error: System.SomeException Followed by slightly bigger stack trace.   These were all the same exception. The problem with this approach is that the error log, if you run any kind of analytics on in, becomes skewed depending on how far up the stack trace your exception was thrown. To mitigate this problem, we came up with the concept of the “PreLoggedException”. Basically, we would log the exception at the very top level and subsequently throw the exception back up the stack encapsulated in this pre-logged type, which our logging system knew to ignore. Now the error log looked like this: Error: System.SomeException Followed by small stack trace. Much cleaner, right? Well, there’s still a problem. When your exception happens in production and you go about trying to figure out what happened, you’ve lost more or less all context for where and how this exception was thrown, because all you really know is what method it was thrown in, but really nothing about who was calling the method or why. What gives you this clue is the entire stack trace, which we’re losing here. I believe that was further mitigated by having the logging system pull a system stack trace and add it to the log entry, but what you’re actually getting is the stack for how you got to the logging code. You’re still losing context about the actual error. Not to mention you’re executing a whole slew of catch blocks which are sloooooooowwwww……… In other words, we started with a bad idea and kept band-aiding it until it didn’t suck quite so bad. When I argued for not catching exceptions at every level but rather catching them following a certain set of rules, my co-worker warned me “do yourself a favor, never express that view in any future interviews.” I suppose this is my ultimate dismissal of that advice, but I’m not too worried. My approach for exception handling follows three basic rules: Only catch an exception if 1. You can do something about it. 2. You can add useful information to it. 3. You’re at an application boundary. Here’s what that means: 1. Only catch an exception if you can do something about it. We’ll start with a trivial example of a login system that uses a file. Please, never actually do this in production code, it’s just concocted example. So if our code goes to open a file and the file isn’t there, we get a FileNotFound exception. If the calling code doesn’t know what to do with this, it should bubble up. However, if we know how to create the file from scratch we can create the file and continue on our merry way. When you run into situations like this though, What should really run through your head is “How can I avoid handling an exception at all?” In this case, it’s a trivial matter to simply check for the existence of the file before trying to open it. If we detect that the file isn’t there, we can accomplish the same thing without having to handle in in a catch block. 2. Only catch an exception if you can do something about it. Continuing with the poorly thought out file based login system we contrived in part 1, if the code calls a Login(…) method and the FileNotFound exception is thrown higher up the stack, the code that calls Login must account for a FileNotFound exception. This is kind of counterintuitive because the calling code should not need to know the internals of the Login method, and the data file is an implementation detail. What makes more sense, assuming that we didn’t implement any of the good advice from step 1, is for Login to catch the FileNotFound exception and wrap it in a new exception. For argument’s sake we’ll say LoginSystemFailureException. (Sorry, couldn’t think of anything better at the moment.) This gives us two stack traces, preserving the original stack trace in the inner exception, and also is much more informative to the calling code. 3. Only catch an exception if you’re at an application boundary. At some point we have to catch all the exceptions, even the ones we don’t know what to do with. WinForms, ASP.Net, and most other UI technologies have some kind of built in mechanism for catching unhandled exceptions without fatally terminating the application. It’s still a good idea to somehow gracefully exit the application in this case if possible though, because you can no longer be sure what state your application is in, but nothing annoys a user more than an application just exploding. These unhandled exceptions need to be logged, and this is a good place to catch them. Ideally you never want this option to be exercised, but code as though it will be. When you log these exceptions, give them a “Fatal” status (e.g. Log4Net) and make sure these bugs get handled in your next release. That’s it in a nutshell. If you do it right each exception will only get logged once and with the largest stack trace possible which will make those 2am emergency severity 1 debugging sessions much shorter and less frustrating. Here’s a few people who also have interesting things to say on this topic:  http://blogs.msdn.com/b/ericlippert/archive/2008/09/10/vexing-exceptions.aspx http://www.codeproject.com/Articles/9538/Exception-Handling-Best-Practices-in-NET I know there’s more but I can’t find them at the moment.

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  • Oracle SQL Developer Data Modeler: What Tables Aren’t In At Least One SubView?

    - by thatjeffsmith
    Organizing your data model makes the information easier to consume. One of the organizational tools provided by Oracle SQL Developer Data Modeler is the ‘SubView.’ In a nutshell, a SubView is a subset of your model. The Challenge: I’ve just created a model which represents my entire ____________ application. We’ll call it ‘residential lending.’ Instead of having all 100+ tables in a single model diagram, I want to break out the tables by module, e.g. appraisals, credit reports, work histories, customers, etc. I’ve spent several hours breaking out the tables to one or more SubViews, but I think i may have missed a few. Is there an easy way to see what tables aren’t in at least ONE subview? The Answer Yes, mostly. The mostly comes about from the way I’m going to accomplish this task. It involves querying the SQL Developer Data Modeler Reporting Schema. So if you don’t have the Reporting Schema setup, you’ll need to do so. Got it? Good, let’s proceed. Before you start querying your Reporting Schema, you might need a data model for the actual reporting schema…meta-meta data! You could reverse engineer the data modeler reporting schema to a new data model, or you could just reference the PDFs in \datamodeler\reports\Reporting Schema diagrams directory. Here’s a hint, it’s THIS one The Query Well, it’s actually going to be at least 2 queries. We need to get a list of distinct designs stored in your repository. For giggles, I’m going to get a listing including each version of the model. So I can query based on design and version, or in this case, timestamp of when it was added to the repository. We’ll get that from the DMRS_DESIGNS table: SELECT DISTINCT design_name, design_ovid, date_published FROM DMRS_designs Then I’m going to feed the design_ovid, down to a subquery for my child report. select name, count(distinct diagram_id) from DMRS_DIAGRAM_ELEMENTS where design_ovid = :dESIGN_OVID and type = 'Table' group by name having count(distinct diagram_id) < 2 order by count(distinct diagram_id) desc Each diagram element has an entry in this table, so I need to filter on type=’Table.’ Each design has AT LEAST one diagram, the master diagram. So any relational table in this table, only having one listing means it’s not in any SubViews. If you have overloaded object names, which is VERY possible, you’ll want to do the report off of ‘OBJECT_ID’, but then you’ll need to correlate that to the NAME, as I doubt you’re so intimate with your designs that you recognize the GUIDs So I’m going to cheat and just stick with names, but I think you get the gist. My Model Of my almost 90 tables, how many of those have I not added to at least one SubView? Now let’s run my report! Voila! My ‘BEER2′ table isn’t in any SubView! It says ’1′ because the main model diagram counts as a view. So if the count came back as ’2′, that would mean the table was in the main model diagram and in 1 SubView diagram. And I know what you’re thinking, what kind of residential lending program would have a table called ‘BEER2?’ Let’s just say, that my business model has some kinks to work out!

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  • Refreshing imported MySQL data with MySQL for Excel

    - by Javier Rivera
    Welcome to another blog post from the MySQL for Excel Team. Today we're going to talk about a new feature included since MySQL for Excel 1.3.0, you can install the latest GA or maintenance version using the MySQL Installer or optionally you can download directly any GA or non-GA version from the MySQL Developer Zone.As some users suggested in our forums we should be maintaining the link between tables and Excel not only when editing data through the Edit MySQL Data option, but also when importing data via Import MySQL Data. Before 1.3.0 this process only provided you with an offline copy of the Table's data into Excel and you had no way to refresh that information from the DB later on. Now, with this new feature we'll show you how easy is to work with the latest available information at all times. This feature is transparent to you (it doesn't require additional steps to work as long as the users had the Create an Excel Table for the imported MySQL table data option enabled. To ensure you have this option checked, click over Advanced Options... after the Import Data dialog is displayed). The current blog post assumes you already know how to import data into excel, you could always take a look at our previous post How To - Guide to Importing Data from a MySQL Database to Excel using MySQL for Excel if you need further reference on that topic. After importing Data from a MySQL Table into Excel, you can refresh the data in 3 ways.1. Simply right click over the range of the imported data, to show the pop-up menu: Click over the Refresh button to obtain the latest copy of the data in the table. 2. Click the Refresh button on the Data ribbon: 3. Click the Refresh All button in the Data ribbon (beware this will refresh all Excel tables in the Workbook): Please take a note of a couple of details here, the first one is about the size of the table. If by the time you refresh the table new columns had been added to it, and you originally have imported all columns, the table will grow to the right. The same applies to rows, if the table has new rows and you did not limit the results , the table will grow to to the bottom of the sheet in Excel. The second detail you should take into account is this operation will overwrite any changes done to the cells after the table was originally imported or previously refreshed: Now with this new feature, imported data remains linked to the data source and is available to be updated at all times. It empowers the user to always be able to work with the latest version of the imported MySQL data. We hope you like this this new feature and give it a try! Remember that your feedback is very important for us, so drop us a message with your comments, suggestions for this or other features and follow us at our social media channels: MySQL on Windows (this) Blog: https://blogs.oracle.com/MySqlOnWindows/ MySQL for Excel forum: http://forums.mysql.com/list.php?172 Facebook: http://www.facebook.com/mysql YouTube channel: https://www.youtube.com/user/MySQLChannel Thanks!

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  • Analyzing data from same tables in diferent db instances.

    - by Oscar Reyes
    Short version: How can I map two columns from table A and B if they both have a common identifier which in turn may have two values in column C Lets say: A --- 1 , 2 B --- ? , 3 C ----- 45, 2 45, 3 Using table C I know that id 2 and 3 belong to the same item ( 45 ) and thus "?" in table B should be 1. What query could do something like that? EDIT Long version ommited. It was really boring/confusing EDIT I'm posting some output here. From this query: select distinct( rolein) , activityin from taskperformance@dm_prod where activityin in ( select activityin from activities@dm_prod where activityid in ( select activityid from activities@dm_prod where activityin in ( select distinct( activityin ) from taskperformance where rolein = 0 ) ) ) I have the following parts: select distinct( activityin ) from taskperformance where rolein = 0 Output: http://question1337216.pastebin.com/f5039557 select activityin from activities@dm_prod where activityid in ( select activityid from activities@dm_prod where activityin in ( select distinct( activityin ) from taskperformance where rolein = 0 ) ) Output: http://question1337216.pastebin.com/f6cef9393 And finally: select distinct( rolein) , activityin from taskperformance@dm_prod where activityin in ( select activityin from activities@dm_prod where activityid in ( select activityid from activities@dm_prod where activityin in ( select distinct( activityin ) from taskperformance where rolein = 0 ) ) ) Output: http://question1337216.pastebin.com/f346057bd Take for instace activityin 335 from first query ( from taskperformance B) . It is present in actvities from A. But is not in taskperformace in A ( but a the related activities: 92, 208, 335, 595 ) Are present in the result. The corresponding role in is: 1

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  • Extract wrong data from a frame in C?

    - by ipkiss
    I am writing a program that reads the data from the serial port on Linux. The data are sent by another device with the following frame format: |start | Command | Data | CRC | End | |0x02 | 0x41 | (0-127 octets) | | 0x03| ---------------------------------------------------- The Data field contains 127 octets as shown and octet 1,2 contains one type of data; octet 3,4 contains another data. I need to get these data. Because in C, one byte can only holds one character and in the start field of the frame, it is 0x02 which means STX which is 3 characters. So, in order to test my program, On the sender side, I construct an array as the frame formatted above like: char frame[254]; frame[0] = 0x02; // starting field frame[1] = 0x41; // command field which is character 'A' ..so on.. And, then On the receiver side, I take out the fields like: char result[254]; // read data read(result); printf("command = %c", result[1]); // get the command field of the frame // get other field's values the command field value (result[1]) is not character 'A'. I think, this because the first field value of the frame is 0x02 (STX) occupying 3 first places in the array frame and leading to the wrong results on the receiver side. How can I correct the issue or am I doing something wrong at the sender side? Thanks all. related questions: http://stackoverflow.com/questions/2500567/parse-and-read-data-frame-in-c http://stackoverflow.com/questions/2531779/clear-data-at-serial-port-in-linux-in-c

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  • Fraud Detection with the SQL Server Suite Part 1

    - by Dejan Sarka
    While working on different fraud detection projects, I developed my own approach to the solution for this problem. In my PASS Summit 2013 session I am introducing this approach. I also wrote a whitepaper on the same topic, which was generously reviewed by my friend Matija Lah. In order to spread this knowledge faster, I am starting a series of blog posts which will at the end make the whole whitepaper. Abstract With the massive usage of credit cards and web applications for banking and payment processing, the number of fraudulent transactions is growing rapidly and on a global scale. Several fraud detection algorithms are available within a variety of different products. In this paper, we focus on using the Microsoft SQL Server suite for this purpose. In addition, we will explain our original approach to solving the problem by introducing a continuous learning procedure. Our preferred type of service is mentoring; it allows us to perform the work and consulting together with transferring the knowledge onto the customer, thus making it possible for a customer to continue to learn independently. This paper is based on practical experience with different projects covering online banking and credit card usage. Introduction A fraud is a criminal or deceptive activity with the intention of achieving financial or some other gain. Fraud can appear in multiple business areas. You can find a detailed overview of the business domains where fraud can take place in Sahin Y., & Duman E. (2011), Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 Vol 1. Hong Kong: IMECS. Dealing with frauds includes fraud prevention and fraud detection. Fraud prevention is a proactive mechanism, which tries to disable frauds by using previous knowledge. Fraud detection is a reactive mechanism with the goal of detecting suspicious behavior when a fraudster surpasses the fraud prevention mechanism. A fraud detection mechanism checks every transaction and assigns a weight in terms of probability between 0 and 1 that represents a score for evaluating whether a transaction is fraudulent or not. A fraud detection mechanism cannot detect frauds with a probability of 100%; therefore, manual transaction checking must also be available. With fraud detection, this manual part can focus on the most suspicious transactions. This way, an unchanged number of supervisors can detect significantly more frauds than could be achieved with traditional methods of selecting which transactions to check, for example with random sampling. There are two principal data mining techniques available both in general data mining as well as in specific fraud detection techniques: supervised or directed and unsupervised or undirected. Supervised techniques or data mining models use previous knowledge. Typically, existing transactions are marked with a flag denoting whether a particular transaction is fraudulent or not. Customers at some point in time do report frauds, and the transactional system should be capable of accepting such a flag. Supervised data mining algorithms try to explain the value of this flag by using different input variables. When the patterns and rules that lead to frauds are learned through the model training process, they can be used for prediction of the fraud flag on new incoming transactions. Unsupervised techniques analyze data without prior knowledge, without the fraud flag; they try to find transactions which do not resemble other transactions, i.e. outliers. In both cases, there should be more frauds in the data set selected for checking by using the data mining knowledge compared to selecting the data set with simpler methods; this is known as the lift of a model. Typically, we compare the lift with random sampling. The supervised methods typically give a much better lift than the unsupervised ones. However, we must use the unsupervised ones when we do not have any previous knowledge. Furthermore, unsupervised methods are useful for controlling whether the supervised models are still efficient. Accuracy of the predictions drops over time. Patterns of credit card usage, for example, change over time. In addition, fraudsters continuously learn as well. Therefore, it is important to check the efficiency of the predictive models with the undirected ones. When the difference between the lift of the supervised models and the lift of the unsupervised models drops, it is time to refine the supervised models. However, the unsupervised models can become obsolete as well. It is also important to measure the overall efficiency of both, supervised and unsupervised models, over time. We can compare the number of predicted frauds with the total number of frauds that include predicted and reported occurrences. For measuring behavior across time, specific analytical databases called data warehouses (DW) and on-line analytical processing (OLAP) systems can be employed. By controlling the supervised models with unsupervised ones and by using an OLAP system or DW reports to control both, a continuous learning infrastructure can be established. There are many difficulties in developing a fraud detection system. As has already been mentioned, fraudsters continuously learn, and the patterns change. The exchange of experiences and ideas can be very limited due to privacy concerns. In addition, both data sets and results might be censored, as the companies generally do not want to publically expose actual fraudulent behaviors. Therefore it can be quite difficult if not impossible to cross-evaluate the models using data from different companies and different business areas. This fact stresses the importance of continuous learning even more. Finally, the number of frauds in the total number of transactions is small, typically much less than 1% of transactions is fraudulent. Some predictive data mining algorithms do not give good results when the target state is represented with a very low frequency. Data preparation techniques like oversampling and undersampling can help overcome the shortcomings of many algorithms. SQL Server suite includes all of the software required to create, deploy any maintain a fraud detection infrastructure. The Database Engine is the relational database management system (RDBMS), which supports all activity needed for data preparation and for data warehouses. SQL Server Analysis Services (SSAS) supports OLAP and data mining (in version 2012, you need to install SSAS in multidimensional and data mining mode; this was the only mode in previous versions of SSAS, while SSAS 2012 also supports the tabular mode, which does not include data mining). Additional products from the suite can be useful as well. SQL Server Integration Services (SSIS) is a tool for developing extract transform–load (ETL) applications. SSIS is typically used for loading a DW, and in addition, it can use SSAS data mining models for building intelligent data flows. SQL Server Reporting Services (SSRS) is useful for presenting the results in a variety of reports. Data Quality Services (DQS) mitigate the occasional data cleansing process by maintaining a knowledge base. Master Data Services is an application that helps companies maintaining a central, authoritative source of their master data, i.e. the most important data to any organization. For an overview of the SQL Server business intelligence (BI) part of the suite that includes Database Engine, SSAS and SSRS, please refer to Veerman E., Lachev T., & Sarka D. (2009). MCTS Self-Paced Training Kit (Exam 70-448): Microsoft® SQL Server® 2008 Business Intelligence Development and Maintenance. MS Press. For an overview of the enterprise information management (EIM) part that includes SSIS, DQS and MDS, please refer to Sarka D., Lah M., & Jerkic G. (2012). Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft® SQL Server® 2012. O'Reilly. For details about SSAS data mining, please refer to MacLennan J., Tang Z., & Crivat B. (2009). Data Mining with Microsoft SQL Server 2008. Wiley. SQL Server Data Mining Add-ins for Office, a free download for Office versions 2007, 2010 and 2013, bring the power of data mining to Excel, enabling advanced analytics in Excel. Together with PowerPivot for Excel, which is also freely downloadable and can be used in Excel 2010, is already included in Excel 2013. It brings OLAP functionalities directly into Excel, making it possible for an advanced analyst to build a complete learning infrastructure using a familiar tool. This way, many more people, including employees in subsidiaries, can contribute to the learning process by examining local transactions and quickly identifying new patterns.

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  • Oracle Data Integrator at Oracle OpenWorld 2012: Demonstrations

    - by Irem Radzik
    By Mike Eisterer Oracle OpenWorld is just a few days away and  we look forward to showing Oracle Data Integrator' comprehensive data integration platform, which delivers critical data integration requirements: from high-volume, high-performance batch loads, to event-driven, trickle-feed integration processes, to SOA-enabled data services.  Several Oracle Data Integrator demonstrations will be available October 1st through the3rd : Oracle Data Integrator and Oracle GoldenGate for Oracle Applications, in Moscone South, Right - S-240 Oracle Data Integrator and Service Integration, in Moscone South, Right - S-235 Oracle Data Integrator for Big Data, in Moscone South, Right - S-236 Oracle Data Integrator for Enterprise Data Warehousing, in Moscone South, Right - S-238 Additional information about OOW 2012 may be found for the following demonstrations. If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.  

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  • Which algorithms/data structures should I "recognize" and know by name?

    - by Earlz
    I'd like to consider myself a fairly experienced programmer. I've been programming for over 5 years now. My weak point though is terminology. I'm self-taught, so while I know how to program, I don't know some of the more formal aspects of computer science. So, what are practical algorithms/data structures that I could recognize and know by name? Note, I'm not asking for a book recommendation about implementing algorithms. I don't care about implementing them, I just want to be able to recognize when an algorithm/data structure would be a good solution to a problem. I'm asking more for a list of algorithms/data structures that I should "recognize". For instance, I know the solution to a problem like this: You manage a set of lockers labeled 0-999. People come to you to rent the locker and then come back to return the locker key. How would you build a piece of software to manage knowing which lockers are free and which are in used? The solution, would be a queue or stack. What I'm looking for are things like "in what situation should a B-Tree be used -- What search algorithm should be used here" etc. And maybe a quick introduction of how the more complex(but commonly used) data structures/algorithms work. I tried looking at Wikipedia's list of data structures and algorithms but I think that's a bit overkill. So I'm looking more for what are the essential things I should recognize?

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  • Understanding Data Science: Recent Studies

    - by Joe Lamantia
    If you need such a deeper understanding of data science than Drew Conway's popular venn diagram model, or Josh Wills' tongue in cheek characterization, "Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician." two relatively recent studies are worth reading.   'Analyzing the Analyzers,' an O'Reilly e-book by Harlan Harris, Sean Patrick Murphy, and Marck Vaisman, suggests four distinct types of data scientists -- effectively personas, in a design sense -- based on analysis of self-identified skills among practitioners.  The scenario format dramatizes the different personas, making what could be a dry statistical readout of survey data more engaging.  The survey-only nature of the data,  the restriction of scope to just skills, and the suggested models of skill-profiles makes this feel like the sort of exercise that data scientists undertake as an every day task; collecting data, analyzing it using a mix of statistical techniques, and sharing the model that emerges from the data mining exercise.  That's not an indictment, simply an observation about the consistent feel of the effort as a product of data scientists, about data science.  And the paper 'Enterprise Data Analysis and Visualization: An Interview Study' by researchers Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffery Heer considers data science within the larger context of industrial data analysis, examining analytical workflows, skills, and the challenges common to enterprise analysis efforts, and identifying three archetypes of data scientist.  As an interview-based study, the data the researchers collected is richer, and there's correspondingly greater depth in the synthesis.  The scope of the study included a broader set of roles than data scientist (enterprise analysts) and involved questions of workflow and organizational context for analytical efforts in general.  I'd suggest this is useful as a primer on analytical work and workers in enterprise settings for those who need a baseline understanding; it also offers some genuinely interesting nuggets for those already familiar with discovery work. We've undertaken a considerable amount of research into discovery, analytical work/ers, and data science over the past three years -- part of our programmatic approach to laying a foundation for product strategy and highlighting innovation opportunities -- and both studies complement and confirm much of the direct research into data science that we conducted. There were a few important differences in our findings, which I'll share and discuss in upcoming posts.

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  • IOMEGA 500GB hard disk data reccovery

    - by Vineeth
    Last year by November I bought an IOMEGA 500GB Prestige hard disk. Yesterday, unfortunately the hard disk fell down from my table. After that incident, when I connect my disk, Windows asks me to format the disk to use, but I didn't format it yet. Actually, on that hard disk I have about 320GB of data. I tried all my possible ways to access my disk. I tried using DOS. It shows "data error (Cyclic redundancy check)". I have a 3 year warranty. Will I be covered under warranty if I report this issue to IOMEGA? Can I get my data back?

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  • how to find a good data center?

    - by drewda
    At my start-up, we're getting to the point where we should be hosting our servers at a data center. I'd appreciate any tips and tricks y'all can offer on finding a reputable place to colocate our racks. Are there any Web sites with customer reviews of data centers or should I just be asking around at techie events? Are unlimited bandwidth plans a gimmick or becoming the norm? Is it worth establishing a redundant set of machines at a second data center from Day One? Or just do offsite back-ups? Thanks for your suggestions.

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  • Appending column to a data frame - R

    - by darkie15
    Is it possible to append a column to data frame in the following scenario? dfWithData <- data.frame(start=c(1,2,3), end=c(11,22,33)) dfBlank <- data.frame() ..how to append column start from dfWithData to dfBlank? It looks like the data should be added when data frame is being initialized. I can do this: dfBlank <- data.frame(dfWithData[1]) but I am more interested if it is possible to append columns to an empty (but inti)

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