Detecting Redundancy in Data Warehouse Evolution

A Data Warehouse (DW) can be abstractly seen as a set of materialized views de ned over a set of remote data sources. A DW is intended to satisfy a set of queries. The views materialized in a DW relate to each other in a complex manner, through common subexpressions, in order to guarantee high query performance and low view maintenance cost. DWs are time varying. As time passes new materialized views are added in order to satisfy new queries or for performance reasons while old queries are dropped. The evolution of a DW can result in a redundant set of materialized views. In this paper we address the problem of detecting redundant views in a given DW view selection, that is, views that can be removed from the DW without negatively a ecting the query evaluation or the view maintenance process. Using an AND/OR dag representation for multiple queries and views, we rst provide a method for detecting materialized views that are not needed in the process of propagating source relation changes to the DW. Then, we use this method to detect materialized views that are redundant. As a side e ect, our approach shows how source relation changes can be propagated to the DW materialized views by exploiting common subexpressions between views and by using other materialized views that are not a ected by these changes.