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Integration of dissimilar structured data Sources through data integration software

Posted On : Jul-29-2011 | seen (867) times | Article Word Count : 675 |

Much of the work of launching a data integration application using traditional approaches is creating an arbitrated schema and semantic mappings to it from the numerous data sources. Though in most of the circumstances contexts do not require full integration
Much of the work of launching a data integration application using traditional approaches is creating an arbitrated schema and semantic mappings to it from the numerous data sources. Though in most of the circumstances contexts do not require full integration in order to start offering valuable data services on a best-effort basis, a so-called pay-as-you-go method to data integration.

Much work towards automatic data integration has been on expanding the expressive power of semantic mappings to permit symbol of uncertainty in these mappings as probabilistic schema. Data extracted using automated techniques may have changing levels of consistency and probabilistic schema permit this uncertainty to be stated, while other database level systems now represent and therefore introduce this ambiguity directly. Formal foundations and query algorithms have been developed to the point where ambiguity can be signified and succeeded professionally in data integration systems.

Why do enterprises develop a system such as a data warehouse or data mart for integrating data? Here are some key factors:


• For the enterprises that have unpredictable data in diverse systems a data warehouse approach can support to clean up these conflicts by offering a single source of certainty.

• Creating a data mart or warehouse improves performance hits on the transactional systems while improving query and report performance for decision support.

• Though end-user software’s and applications can join data from dissimilar sources, it is frequently a composite job requiring time and performance.

• Corrupt data leads to bad results. A data warehouse method offers the chance for enterprises to examine and accurate bad data such as name and addresses.

• Most operative systems such as a CRM or ERP systems deliver only customer transactions and inquiries that last up to one to two years. Old data is archived. If anybody want to investigate customer behavior over a long period of time, you will need a system that can support large amounts of data.

• A data warehouse delivers the arrangement for better safety procedures.

• Operational systems are often varying. Running reports at dissimilar times can effect in different outcomes. With BI users accessing integrated data, they will know when it has been restored and they will have access to antique information.

• A data warehouse permits enterprises data integration from external sources so it can be shared, streamlined, cleansed, and accumulated in a single system that can deliver a complete enterprise view.


• Most operative systems such as a CRM or ERP systems deliver only customer transactions and inquiries that last up to one to two years. Old data is archived. If anybody want to investigate customer behavior over a long period of time, you will need a system that can support large amounts of data.

• A data warehouse delivers the arrangement for better safety procedures.

• Operational systems are often varying. Running reports at dissimilar times can effect in different outcomes. With BI users accessing integrated data, they will know when it has been restored and they will have access to antique information.

• A data warehouse permits enterprises data integration from external sources so it can be shared, streamlined, cleansed, and accumulated in a single system that can deliver a complete enterprise view.

Article Source : http://www.articleseen.com/Article_ Integration of dissimilar structured data Sources through data integration software_69686.aspx

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Keywords : data integration, application integration,

Category : Computers : Software

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