Database System Extensions for Decision Support: the AXL Approach

Research on database-centric data mining is seeking to improve the e ectiveness of database systems in decision support applications. Di erent solutions are now used for di erent problems, including (i) SQL extensions for more complex OLAP queries, (ii) new datablades for special data types such as time-series, and (iii) architectural extensions to support data mining functions. Here, we proposed a uni ed solution for all these problems; the solution is based on User-De ned Aggregates (UDAs) expressed in an SQL-like language called AXL. In this paper, we discuss the architecture and implementation of the AXL prototype and its use and performance in expressing data mining functions and complex OLAP queries.

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