If you have not noticed, the concepts of enterprise application integration (EAI), and the world of business intelligence and data warehousing are merging. The intersection of the technologies is the interest in information that exists in many different types of systems. Both paradigms require connector technology to account for the differences in how the information is physically presented. Both paradigms must transform the information. Data integration needs to transform the information moving between systems - one-to-one or many-to-many - in real time, accounting for the differences in application semantics during replication. Business intelligence (BI) transforms and aggregates the information in large batches, placing the information in a specialized data store for data mining activities. Moreover, both paradigms must provide mechanisms to make sense of the information; data integration must do so in real time (business activity monitoring or BAM), and BI typically does so with latency.

The largest intersection of data integration and BI is the need to understand the data that already exists within the source and target systems. To do this, many have been looking toward ontologies, which provide a sophisticated approach to accounting for, categorizing and drawing relationships between the thousands of ways data exists inside a typical enterprise or problem domain.

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