Active Data Warehouses: Complementing OLAP with Active Rules

Conventional data warehouses are passive. All tasks related to analysing data and making decisions must be carried out manually by analysts. Today's data warehouse and OLAP systems offer little support to automatize decision tasks that occur frequently and for which well established decision procedures are available. Such a functionality can be provided by extending the conventional data warehouse architecture with analysis rules, which mimic the work of an analyst during decision making. Analysis rules extend the basic ECA (event/condition/action) rule structure with mechanisms to analyse data multidimensionally and to make decisions. The resulting architecture is called active data warehouse. This paper (1) motivates the need for active data warehouses, (2) discusses various issues of analysis rules and provides a textual language for their specification, and (3) outlines how analysis rules can be implemented on top of existing relational data warehouses.