We consider the problem of improving the efficiency of query processing on an XML interface of a relational database, for predefined query workloads. The main contribution of this paper is to show that selective materialization of data as XML views reduces query-execution costs in relatively static databases. Our learning-based approach precomputes and stores (materializes) parts of the answers to the workload queries as clustered XML views. In addition, the data in the materialized XML clusters are periodically incrementally refreshed and rearranged, to respond to the changes in the query workload. Our experiments show that the approach can significantly reduce processing costs for frequent and important queries on relational databases with XML interfaces.
REF use materialized XML views to improve query performance over relational databases.
R.: Improving Query Performance Using Materialized XML Views: A Learning-Based Approach
In Proc. 1st Int. Workshop on XML Schema and Data Management (XSDM), LNCS 2814
Computer Science