Improving Query Performance using Materialized XML Views: A Learning-based approach
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Date
2004-06-18
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Abstract
This thesis presents a novel approach in solving the problem of improving the efficiency of query processing on an XML interface of a relational database for frequent and important queries. The motivation of this research is provided by the need to eliminate processing overheads in converting relational data to an XML format by materializing beforehand answers to frequent and important queries (which we predefine as a query workload) in terms of an XML structure. The main contribution of this paper is to show that selective materialization of data as XML views reduces query-execution costs for the workload queries, 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. We use a collection of music data as a sample database to build our learning-based system. Our experiments show that the approach can significantly reduce processing costs for frequent and important queries on relational databases with XML interfaces.
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xml, views, materialization, learning
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Degree
MS
Discipline
Computer Science
