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  • BizTalk Cross Reference Data Management Strategy

    - by charlie.mott
    Article Source: http://geekswithblogs.net/charliemott This article describes an approach to the management of cross reference data for BizTalk.  Some articles about the BizTalk Cross Referencing features can be found here: http://home.comcast.net/~sdwoodgate/xrefseed.zip http://geekswithblogs.net/michaelstephenson/archive/2006/12/24/101995.aspx http://geekswithblogs.net/charliemott/archive/2009/04/20/value-vs.id-cross-referencing-in-biztalk.aspx Options Current options to managing this data include: Maintaining xml files in the format that can be used by the out-of-the-box BTSXRefImport.exe utility. Use of user interfaces that have been developed to manage this data: BizTalk Cross Referencing Tool XRef XML Creation Tool However, there are the following issues with the above options: The 'BizTalk Cross Referencing Tool' requires a separate database to manage.  The 'XRef XML Creation' tool has no means of persisting the data settings. The 'BizTalk Cross Referencing tool' generates integers in the common id field. I prefer to use a string (e.g. acme.country.uk). This is more readable. (see naming conventions below). Both UI tools continue to use BTSXRefImport.exe.  This utility replaces all xref data. This can be a problem in continuous integration environments that support multiple clients or BizTalk target instances.  If you upload the data for one client it would destroy the data for another client.  Yet in TFS where builds run concurrently, this would break unit tests. Alternative Approach In response to these issues, I instead use simple SQL scripts to directly populate the BizTalkMgmtDb xref tables combined with a data namepacing strategy to isolate client data. Naming Conventions All data keys use namespace prefixing.  The pattern will be <companyName>.<data Type>.  The naming conventions will be to use lower casing for all items.  The data must follow this pattern to isolate it from other company cross-reference data.  The table below shows some sample data. (Note: this data uses the 'ID' cross-reference tables.  the same principles apply for the 'value' cross-referencing tables). Table.Field Description Sample Data xref_AppType.appType Application Types acme.erp acme.portal acme.assetmanagement xref_AppInstance.appInstance Application Instances (each will have a corresponding application type). acme.dynamics.ax acme.dynamics.crm acme.sharepoint acme.maximo xref_IDXRef.idXRef Holds the cross reference data types. acme.taxcode acme.country xref_IDXRefData.CommonID Holds each cross reference type value used by the canonical schemas. acme.vatcode.exmpt acme.vatcode.std acme.country.usa acme.country.uk xref_IDXRefData.AppID This holds the value for each application instance and each xref type. GBP USD SQL Scripts The data to be stored in the BizTalkMgmtDb xref tables will be managed by SQL scripts stored in a database project in the visual studio solution. File(s) Description Build.cmd A sqlcmd script to deploy data by running the SQL scripts below.  (This can be run as part of the MSBuild process).   acme.purgexref.sql SQL script to clear acme.* data from the xref tables.  As such, this will not impact data for any other company. acme.applicationInstances.sql   SQL script to insert application type and application instance data.   acme.vatcode.sql acme.country.sql etc ...  There will be a separate SQL script to insert each cross-reference data type and application specific values for these types.

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  • Master Note for Generic Data Warehousing

    - by lajos.varady(at)oracle.com
    ++++++++++++++++++++++++++++++++++++++++++++++++++++ The complete and the most recent version of this article can be viewed from My Oracle Support Knowledge Section. Master Note for Generic Data Warehousing [ID 1269175.1] ++++++++++++++++++++++++++++++++++++++++++++++++++++In this Document   Purpose   Master Note for Generic Data Warehousing      Components covered      Oracle Database Data Warehousing specific documents for recent versions      Technology Network Product Homes      Master Notes available in My Oracle Support      White Papers      Technical Presentations Platforms: 1-914CU; This document is being delivered to you via Oracle Support's Rapid Visibility (RaV) process and therefore has not been subject to an independent technical review. Applies to: Oracle Server - Enterprise Edition - Version: 9.2.0.1 to 11.2.0.2 - Release: 9.2 to 11.2Information in this document applies to any platform. Purpose Provide navigation path Master Note for Generic Data Warehousing Components covered Read Only Materialized ViewsQuery RewriteDatabase Object PartitioningParallel Execution and Parallel QueryDatabase CompressionTransportable TablespacesOracle Online Analytical Processing (OLAP)Oracle Data MiningOracle Database Data Warehousing specific documents for recent versions 11g Release 2 (11.2)11g Release 1 (11.1)10g Release 2 (10.2)10g Release 1 (10.1)9i Release 2 (9.2)9i Release 1 (9.0)Technology Network Product HomesOracle Partitioning Advanced CompressionOracle Data MiningOracle OLAPMaster Notes available in My Oracle SupportThese technical articles have been written by Oracle Support Engineers to provide proactive and top level information and knowledge about the components of thedatabase we handle under the "Database Datawarehousing".Note 1166564.1 Master Note: Transportable Tablespaces (TTS) -- Common Questions and IssuesNote 1087507.1 Master Note for MVIEW 'ORA-' error diagnosis. For Materialized View CREATE or REFRESHNote 1102801.1 Master Note: How to Get a 10046 trace for a Parallel QueryNote 1097154.1 Master Note Parallel Execution Wait Events Note 1107593.1 Master Note for the Oracle OLAP OptionNote 1087643.1 Master Note for Oracle Data MiningNote 1215173.1 Master Note for Query RewriteNote 1223705.1 Master Note for OLTP Compression Note 1269175.1 Master Note for Generic Data WarehousingWhite Papers Transportable Tablespaces white papers Database Upgrade Using Transportable Tablespaces:Oracle Database 11g Release 1 (February 2009) Platform Migration Using Transportable Database Oracle Database 11g and 10g Release 2 (August 2008) Database Upgrade using Transportable Tablespaces: Oracle Database 10g Release 2 (April 2007) Platform Migration using Transportable Tablespaces: Oracle Database 10g Release 2 (April 2007)Parallel Execution and Parallel Query white papers Best Practices for Workload Management of a Data Warehouse on the Sun Oracle Database Machine (June 2010) Effective resource utilization by In-Memory Parallel Execution in Oracle Real Application Clusters 11g Release 2 (Feb 2010) Parallel Execution Fundamentals in Oracle Database 11g Release 2 (November 2009) Parallel Execution with Oracle Database 10g Release 2 (June 2005)Oracle Data Mining white paper Oracle Data Mining 11g Release 2 (March 2010)Partitioning white papers Partitioning with Oracle Database 11g Release 2 (September 2009) Partitioning in Oracle Database 11g (June 2007)Materialized Views and Query Rewrite white papers Oracle Materialized Views  and Query Rewrite (May 2005) Improving Performance using Query Rewrite in Oracle Database 10g (December 2003)Database Compression white papers Advanced Compression with Oracle Database 11g Release 2 (September 2009) Table Compression in Oracle Database 10g Release 2 (May 2005)Oracle OLAP white papers On-line Analytic Processing with Oracle Database 11g Release 2 (September 2009) Using Oracle Business Intelligence Enterprise Edition with the OLAP Option to Oracle Database 11g (July 2008)Generic white papers Enabling Pervasive BI through a Practical Data Warehouse Reference Architecture (February 2010) Optimizing and Protecting Storage with Oracle Database 11g Release 2 (November 2009) Oracle Database 11g for Data Warehousing and Business Intelligence (August 2009) Best practices for a Data Warehouse on Oracle Database 11g (September 2008)Technical PresentationsA selection of ObE - Oracle by Examples documents: Generic Using Basic Database Functionality for Data Warehousing (10g) Partitioning Manipulating Partitions in Oracle Database (11g Release 1) Using High-Speed Data Loading and Rolling Window Operations with Partitioning (11g Release 1) Using Partitioned Outer Join to Fill Gaps in Sparse Data (10g) Materialized View and Query Rewrite Using Materialized Views and Query Rewrite Capabilities (10g) Using the SQLAccess Advisor to Recommend Materialized Views and Indexes (10g) Oracle OLAP Using Microsoft Excel With Oracle 11g Cubes (how to analyze data in Oracle OLAP Cubes using Excel's native capabilities) Using Oracle OLAP 11g With Oracle BI Enterprise Edition (Creating OBIEE Metadata for OLAP 11g Cubes and querying those in BI Answers) Building OLAP 11g Cubes Querying OLAP 11g Cubes Creating Interactive APEX Reports Over OLAP 11g CubesSelection of presentations from the BIWA website:Extreme Data Warehousing With Exadata  by Hermann Baer (July 2010) (slides 2.5MB, recording 54MB)Data Mining Made Easy! Introducing Oracle Data Miner 11g Release 2 New "Work flow" GUI   by Charlie Berger (May 2010) (slides 4.8MB, recording 85MB )Best Practices for Deploying a Data Warehouse on Oracle Database 11g  by Maria Colgan (December 2009)  (slides 3MB, recording 18MB, white paper 3MB )

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  • Oracle Enterprise Data Quality: Ever Integration-ready

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

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  • Master Data Management Implementation Styles

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

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  • How to tackle archived who-is personal data with opt-out?

    - by defaye
    As far as I understand it, it is possible to opt-out (in the UK at least) of having your address details displayed on who-is information of a domain for non-trading individuals. What I want to know is, after opt-out, how do individuals combat archived data? Is there any enforcement of this? How many who-is websites are there which archive data and what rights do we have to force them to remove that data without paying absurd fees? In the case of capitulating to these scoundrels, what point is it in paying for the removal of archived data if that data can presumably resurface on another who-is repository? In other words, what strategy is one supposed to take, besides being wiser after the fact?

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  • How to export and import an user profile from one Quassel core to another?

    - by Zertrin
    I have been using Quassel as my bouncer for IRC for quite a long time now. We (a group of administrators of a small network) have set up a shared Quassel core with many users on the same core. But now I would like to export everything related to my user account from the Quassel database on this core, in order to re-import it later in another Quassel core on my own server. Unfortunately, while a feature for adding users has been implemented into Quassel, nothing is so far provided for either exporting or deleting an user. (if deleting-a-user feature was available, I could have made a copy of the current database, delete all the other users leaving only mine, and use this resulting database on my own server, while leaving the first one untouched on the shared server) Despite extensive research on the Internet on this subject, I've found so far no solution. I have to precise that the backend database for the core has been migrated from the default SQLite backend to a PosgreSQL backend as the database grew sensibly (over 1,5 GB for now). However I'd be glad to hear from any working solution (SQLite or PostgreSQL backend) describing a way to export the data related to a specific user profile and then re-import-it in a new Quasselcore database.

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  • How to force user to user Administrator account in WinForms

    - by Smejda
    I have simple WinForms application where modifying Windows Registry. The problem is that in Vista / Windows 7 I need to force user to switch to administrator. I do not want to force user to Run as Administrator form start of the application. I want him to do it when there is necessity to write to registry. Best case scenario would be to reach exacly the same message which appear in lot's of Setups, when user need to 'switch' to Administrator so there is no necessity to Run as Administrator form beginning. How I can achieve this in .Net ?

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  • How do you encourage users to fill out their profile?

    - by mattdell
    Hello, I wanted to open up the topic to discuss ways to encourage or incentivize users to fill in information in a user profile on a website, such as skills, location, organization, etc. More information in a user profile can give a website an improved capability for its users to search, network, and collaborate. Without bugging users to fill in their profiles (ie - via annoying e-mail reminders), what other ways have you guys come up with to encourage user input? Best, -Matt

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  • User controls in masterpage and anonymous user

    - by Senad Uka
    I am developing a master page which includes the user control that generates a menu from the list with a specific logic. Before including the control into master page I successfully configured anonymous access to the site. After including the control and deploying - site prompts for user name and password. I allowed the anonymous access to the list. Oh yes ... It worked on SHarepoint 2010 beta, but the problem happens when deploying to the Sharepoint 2010 final release. Additional data: I am using Sharepoint Server 2010 with Standard features, standalone instalation on Windows Server 2008 R2 for deployment, and Visual Studio 2010 Ultimate for development of masterpage and user control.

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  • How to record when user follows external links without slowing user down

    - by taw
    I want to track when user clicks external links for analytics purposes. The simplest solution is to replace all external links with links to special record-and-redirect controller, but that would slow the user unnecessarily. The second idea would be to override click event and within in $.post a message to record controller, then let the main event handler happen, which will usually be either click (open link in same tab) or middle click (open in new tab) - good either way, and the user won't have to wait for wait for my server to record it, it's fire-and-forget. (I don't care if users without Javascript don't get tracked) Is that a reasonable way to go? Or what else would be the best way to track all external link clicks?

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  • Refactoring multiple if statements for user authentication with subdomains

    - by go minimal
    I'm building a typical web app where once a user signs up they access the app through their own subdomain (company.myapp.com). The "checking what kind of user if any is logged in" piece is starting to get very hairy and it obviously needs to be well-written because its run so often so I was wondering how you guys would re-factor this stuff. Here are the different states: A user must be logged in, the user must not have a company name, and the sub-domain must be blank A user must be logged in, the user must have a company name, that company name must match the current sub-domain A user must be logged in, the user must have a company name, that company name must match the current sub-domain, and the user's is_admin boolean is true if !session[:user_id].nil? @user = User.find(session[:user_id]) if @user.company.nil? && request.subdomains.first.nil? return "state1" elsif [email protected]? if @user.company.downcase == request.subdomains.first.downcase && [email protected]_admin return "state2" elsif @user.company.downcase == request.subdomains.first.downcase && @user.is_admin return "state3" end end end

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  • MS Dynamics CRM users disappear

    - by Max Kosyakov
    Recently we came across quite a weird issue. The administrators say that once in a while they notice that user accounts in MS Dynamics CRM are lost . When a new user is added to the system, the administrators add him/her to the Active Directory first. Then, they go to Dynamics CRM interface, then to system configuration -> administration -> users and add the new user to the CRM, add roles to this user, grant them relevant permissions. Then the user is able to use a custom application, which connects to the Dynamics CRM via WCF. After a while (few weeks or months) the user is unable to use the custom application because Dynamics CRM cannot authorise this user. When administrators open the Dynamics CRM user management interface (configuration -> administration -> users ) and browse through the list of CRM users they cannot find the user in the list. When they try to add the user to Dynamics CRM back, the CRM fails with the error message "User already exists". Moreover, the user still exists in the Active Directory. The admins are very sure the user had been added to the CRM before he/she started to work. The only fact the the user was able to use the custom application normally says that the user had been indeed registered in the CRM. How come the user is not listed in the CRM user management interface at all? Have anyone faced any issues like that? Seen or heard of disappearing CRM users somewhere? Any help is appreciated. Where can one start digging?

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  • Chef: nested data bag data to template file returns "can't convert String into Integer"

    - by Dalho Park
    I'm creating simple test recipe with a template and data bag. What I'm trying to do is creating a config file from data bag that has simple nested information, but I receive error "can't convert String into Integer" Here are my setting file 1) recipe/default.rb data1 = data_bag_item( 'mytest', 'qa' )['test'] data2 = data_bag_item( 'mytest', 'qa' ) template "/opt/env/test.cfg" do source "test.erb" action :create_if_missing mode 0664 owner "root" group "root" variables({ :pepe1 = data1['part.name'], :pepe2 = data2['transport.tcp.ip2'] }) end 2)my data bag named "mytest" $knife data bag show mytest qa id: qa test: part.name: L12 transport.tcp.ip: 111.111.111.111 transport.tcp.port: 9199 transport.tcp.ip2: 222.222.222.222 3)template file test.erb part.name=<%= @pepe1 % transport.tcp.binding=<%= @pepe2 % Error reurns when I run chef-client on my server, [2013-06-24T19:50:38+00:00] DEBUG: filtered backtrace of compile error: /var/chef/cache/cookbooks/config_test/recipes/default.rb:19:in []',/var/chef/cache/cookbooks/config_test/recipes/default.rb:19:inblock in from_file',/var/chef/cache/cookbooks/config_test/recipes/default.rb:12:in from_file' [2013-06-24T19:50:38+00:00] DEBUG: filtered backtrace of compile error: /var/chef/cache/cookbooks/config_test/recipes/default.rb:19:in[]',/var/chef/cache/cookbooks/config_test/recipes/default.rb:19:in block in from_file',/var/chef/cache/cookbooks/config_test/recipes/default.rb:12:infrom_file' [2013-06-24T19:50:38+00:00] DEBUG: backtrace entry for compile error: '/var/chef/cache/cookbooks/config_test/recipes/default.rb:19:in `[]'' [2013-06-24T19:50:38+00:00] DEBUG: Line number of compile error: '19' Recipe Compile Error in /var/chef/cache/cookbooks/config_test/recipes/default.rb TypeError can't convert String into Integer Cookbook Trace: /var/chef/cache/cookbooks/config_test/recipes/default.rb:19:in []' /var/chef/cache/cookbooks/config_test/recipes/default.rb:19:inblock in from_file' /var/chef/cache/cookbooks/config_test/recipes/default.rb:12:in `from_file' Relevant File Content: /var/chef/cache/cookbooks/config_test/recipes/default.rb: 12: template "/opt/env/test.cfg" do 13: source "test.erb" 14: action :create_if_missing 15: mode 0664 16: owner "root" 17: group "root" 18: variables({ 19 :pepe1 = data1['part.name'], 20: :pepe2 = data2['transport.tcp.ip2'] 21: }) 22: end 23: I tried many things and if I comment out "pepe1 = data1['part.name'],", then :pepe2 = data2['transport.tcp.ip2'] works fine. only nested data "part.name" cannot be set to @pepe1. Does anyone knows why I receive the errors? thanks,

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  • Data take on with Drupal 6

    - by Robert MacLean
    We are migrating our current intranet to Drupal 6 and there is a lot of data within the current system which can be classified into: List data, general lists of fields. Common use is phone list of the employees phone numbers. Document repository. Just basically a web version of a file share for documents. I can easily get the data + meta infomation out, but how do I bulk upload the two types of data into Drupal, as uploading the hundred of thousands of items manually is just not acceptable.

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  • TDWI World Conference Features Oracle and Big Data

    - by Mandy Ho
    Oracle is a Gold Sponsor at this year's TDWI World Conference Series, held at the Manchester Grand Hyatt in San Diego, California - July 31 to Aug 1. The theme of this event is Big Data Tipping Point: BI Strategies in the Era of Big Data. The conference features an educational look at how data is now being generated so quickly that organizations across all industries need new technologies to stay ahead - to understand customer behavior, detect fraud, improve processes and accelerate performance. Attendees will hear how the internet, social media and streaming data are fundamentally changing business intelligence and data warehousing. Big data is reaching critical mass - the tipping point. Oracle will be conducting the following Evening Workshop. To reserve your space, call 1.800.820.5592 ext 10775. Title...:    Integrating Big Data into Your Data Center (or A Big Data Reference Architecture) Date.:    Wed., August 1, 2012, at 7:00 p.m Venue:: Manchester Grand Hyatt, San Diego, Room Weblogs, Social Media, smart meters, senors and other devices generate high volumes of low density information that isn't readily accessible in enterprise data warehouses and business intelligence applications today. But, this data can have relevant business value, especially when analyzed alongside traditional information sources. In this session, we will outline a reference architecture for big data that will help you maximize the value of your big data implementation. You will learn: The key technologies in a big architecture, and their specific use case The integration point of the various technologies and how they fit into your existing IT environment How effectively leverage analytical sandboxes for data discovery and agile development of data driven solutions   At the end of this session you will understand the reference architecture and have the tools to implement this architecture at your company. Presenter: Jean-Pierre Dijcks, Senior Principal Product Manager Don't miss our booth and the chance to meet with our Big data experts on the exhibition floor at booth #306. 

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  • How do you encode Algebraic Data Types in a C#- or Java-like language?

    - by Jörg W Mittag
    There are some problems which are easily solved by Algebraic Data Types, for example a List type can be very succinctly expressed as: data ConsList a = Empty | ConsCell a (ConsList a) consmap f Empty = Empty consmap f (ConsCell a b) = ConsCell (f a) (consmap f b) l = ConsCell 1 (ConsCell 2 (ConsCell 3 Empty)) consmap (+1) l This particular example is in Haskell, but it would be similar in other languages with native support for Algebraic Data Types. It turns out that there is an obvious mapping to OO-style subtyping: the datatype becomes an abstract base class and every data constructor becomes a concrete subclass. Here's an example in Scala: sealed abstract class ConsList[+T] { def map[U](f: T => U): ConsList[U] } object Empty extends ConsList[Nothing] { override def map[U](f: Nothing => U) = this } final class ConsCell[T](first: T, rest: ConsList[T]) extends ConsList[T] { override def map[U](f: T => U) = new ConsCell(f(first), rest.map(f)) } val l = (new ConsCell(1, new ConsCell(2, new ConsCell(3, Empty))) l.map(1+) The only thing needed beyond naive subclassing is a way to seal classes, i.e. a way to make it impossible to add subclasses to a hierarchy. How would you approach this problem in a language like C# or Java? The two stumbling blocks I found when trying to use Algebraic Data Types in C# were: I couldn't figure out what the bottom type is called in C# (i.e. I couldn't figure out what to put into class Empty : ConsList< ??? >) I couldn't figure out a way to seal ConsList so that no subclasses can be added to the hierarchy What would be the most idiomatic way to implement Algebraic Data Types in C# and/or Java? Or, if it isn't possible, what would be the idiomatic replacement?

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  • How should I track approval workflow when users at every security level can create a request?

    - by Eric Belair
    I am writing a new application that allows users to enter requests. Once a request is entered, it must follow an approval workflow to be finally approved by a user the highest security level. So, let's say a user at Security Level 1 enters a request. This request must be approved by his superior - a user at Security Level 2. Once the Security Level 2 user approves it, it must be approved by a user at Security Level 3. Once the Security Level 3 user approves it, it is considered fully approved. However, users at any of the three Security Levels can enter requests. So, if a Security Level 3 user enters a request, it is automatically considered "fully approved". And, if a Security Level 2 user enters a request, it must only be approved by a Security Level 3 user. I'm currently storing each approval status in a Database Log Table, like so: STATUS_ID (PK) REQUEST_ID STATUS STATUS_DATE -------------- ------------- ---------------- ----------------------- 1 1 USER_SUBMIT 2012-09-01 00:00:00.000 2 1 APPROVED_LEVEL2 2012-09-01 01:00:00.000 3 1 APPROVED_LEVEL3 2012-09-01 02:00:00.000 4 2 USER_SUBMIT 2012-09-01 02:30:00.000 5 2 APPROVED_LEVEL2 2012-09-01 02:45:00.000 My question is, which is a better design: Record all three statuses for every request ...or... Record only the statuses needed according to the Security Level of the user submitting the request In Case 2, the data might look like this for two requests - one submitted by Security Level 2 User and another submitted by Security Level 3 user: STATUS_ID (PK) REQUEST_ID STATUS STATUS_DATE -------------- ------------- ---------------- ----------------------- 1 3 APPROVED_LEVEL2 2012-09-01 01:00:00.000 2 3 APPROVED_LEVEL3 2012-09-01 02:00:00.000 3 4 APPROVED_LEVEL3 2012-09-01 02:00:00.000

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  • Why is my ServiceOperation method missing from my WCF Data Services client proxy code?

    - by Kev
    I have a simple WCF Data Services service and I want to expose a Service Operation as follows: [System.ServiceModel.ServiceBehavior(IncludeExceptionDetailInFaults = true)] public class ConfigurationData : DataService<ProductRepository> { // This method is called only once to initialize service-wide policies. public static void InitializeService(IDataServiceConfiguration config) { config.SetEntitySetAccessRule("*", EntitySetRights.ReadMultiple | EntitySetRights.ReadSingle); config.SetServiceOperationAccessRule("*", ServiceOperationRights.All); config.UseVerboseErrors = true; } // This operation isn't getting generated client side [WebGet] public IQueryable<Product> GetProducts() { // Simple example for testing return (new ProductRepository()).Product; } Why isn't the GetProducts method visible when I add the service reference on the client?

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  • How to Convert multiple sets of Data going from left to right to top to bottom the Pythonic way?

    - by ThinkCode
    Following is a sample of sets of contacts for each company going from left to right. ID Company ContactFirst1 ContactLast1 Title1 Email1 ContactFirst2 ContactLast2 Title2 Email2 1 ABC John Doe CEO [email protected] Steve Bern CIO [email protected] How do I get them to go top to bottom as shown? ID Company Contactfirst ContactLast Title Email 1 ABC John Doe CEO [email protected] 1 ABC Steve Bern CIO [email protected] I am hoping there is a Pythonic way of solving this task. Any pointers or samples are really appreciated! p.s : In the actual file, there are 10 sets of contacts going from left to right and there are few thousand such records. It is a CSV file and I loaded into MySQL to manipulate the data.

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  • How to add data manually in core data entity

    - by pankaj
    Hi I am working on core data for the first time. I have just created an entity and attributes for that entity. I want to add some data inside the entity(u can say i want to add data in a table), earlier i when i was using sqlite, i would add data using terminal. But here in core data i am not able to find a place where i can manually add data. I just want to add data in entity and display it in a UITableView. I have gone through the the documentation of core data but it does not explain how to add data manually although it explains how i can add it programmiticaly but i dont need to do it programically. I want to do it manually. Thanks in advance

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  • How do I set default values on new properties for existing entities after light weight core data migration?

    - by Moritz
    I've successfully completed light weight migration on my core data model. My custom entity Vehicle received a new property 'tirePressure' which is an optional property of type double with the default value 0.00. When 'old' Vehicles are fetched from the store (Vehicles that were created before the migration took place) the value for their 'tirePressure' property is nil. (Is that expected behavior?) So I thought: "No problem, I'll just do this in the Vehicle class:" - (void)awakeFromFetch { [super awakeFromFetch]; if (nil == self.tirePressure) { [self willChangeValueForKey:@"tirePressure"]; self.tirePressure = [NSNumber numberWithDouble:0.0]; [self didChangeValueForKey:@"tirePressure"]; } } Since "change processing is explicitly disabled around" awakeFromFetch I thought the calls to willChangeValueForKey and didChangeValueForKey would mark 'tirePresure' as dirty. But they don't. Every time these Vehicles are fetched from the store 'tirePressure' continues to be nil despite having saved the context.

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  • How do you verify the correct data is in a data mart?

    - by blockcipher
    I'm working on a data warehouse and I'm trying to figure out how to best verify that data from our data cleansing (normalized) database makes it into our data marts correctly. I've done some searches, but the results so far talk more about ensuring things like constraints are in place and that you need to do data validation during the ETL process (E.g. dates are valid, etc.). The dimensions were pretty easy as I could easily either leverage the primary key or write a very simple and verifiable query to get the data. The fact tables are more complex. Any thoughts? We're trying to make this very easy for a subject matter export to run a couple queries, see some data from both the data cleansing database and the data marts, and visually compare the two to ensure they are correct.

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  • What is a good approach for a Data Access Layer?

    - by Adil Mughal
    Our software is a customized Human Resource Management System (HRMS) using ASP.NET with Oracle as the database and now we are actually moving to make it a product that supports multiple tenants with their own databases. Our options: Use NHibernate to support Multiple databases and use of OO. But we concern related to NHibernate learning curve and any problem we faced. Make a generalized DAL which will continue working with Oracle using stored procedures and use tools to convert it to other databases such as SQL Server or MySql. There is a risk associated with having to support multiple database-dependent versions of a single script. Provide the software as a Service (SaaS) and maintain the way we conduct business. However there can may be clients who do not want or trust the Cloud or other SaaS business models. With this in mind, what's the best Data access layer technique?

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  • Select data from three different tables with null data

    - by user3678972
    I am new in Sql. My question is how to get data from three different tables with null values. I have tried a query as below: SELECT * FROM [USER] JOIN [Location] ON ([Location].UserId = [USER].Id) JOIN [ParentChild] ON ([ParentChild].UserId = [USER].Id) WHERE ParentId=7 which I find from this link. Its working fine but, it not fetches all and each data associated with the ParentId Something like it only fetches data which are available in all tables, but also omits some data which not available in Location tables but it comes under the given ParentId. For example: UserId ParentId 1 7 8 7 For userId 8, there is data available in Location table,so it fetches all data. But there is no data for userId 1 available in Location table, so the query didn't work for this. But I want all and every data. If there is no data for userId then it can return only null columns. Is it possible ?? hope everyone can understand my problem.

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  • How does Core Data determine if an NSObjects data can be dropped?

    - by Kevin
    In the app I am working on now I was storing about 500 images in Core Data. I have since pulled those images out and store them in the file system now, but in the process I found that the app would crash on the device if I had an array of 500 objects with image data in them. An array with 500 object ids with the image data in those objects worked fine. The 500 objects without the image data also worked fine. I found that I got the best performance with both an array of object ids and image data stored on the filesystem instead of in core data. The conclusion I came to was that if I had an object in an array that told Core Data I was "using" that object and Core Data would hold on to the data. Is this correct?

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