SQL Extensions to Enable Decision Support within Database Systems

Abstract

Modern organizations store massive amount of information within their data warehouses to carry out complex analysis and decision support, such as identifying interesting trends, making unusual patterns to stand out and verifying hypotheses. Besides traditional data warehousing applications, a new class of data-intensive systems is emerging: applications in which the data is modeled not as persistent relations but rather as transient data streams. In this article we approach decision support from the database viewpoint. We briefly mention SQL features – the standard query language of database systems – and review several proposed extensions to make it a decision support-oriented language. Special emphasis is given to EMF SQL, an approach that has proven very useful in complex querying over traditional data ware-housing applications. We argue that this extension can also be useful in the context of stream data. Finally, we review and discuss decision support challenges over data stream systems.