Materialized View Selection by Query Clustering in XML Data Warehouses

TL;DR

This paper presents an automatic method for selecting XML materialized views using query workload clustering, improving performance in XML data warehouses for complex decision-support queries.

Contribution

The paper introduces a novel strategy that leverages data mining, specifically query clustering, to optimize materialized view selection in XML data warehouses.

Findings

The strategy significantly improves query response times.

Experimental validation shows effectiveness even with complex queries.

Implementation on an XCube-modeled warehouse confirms practical benefits.

Abstract

XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native XML database management systems currently bear limited performances and it is necessary to design strategies to optimize them. In this paper, we propose an automatic strategy for the selection of XML materialized views that exploits a data mining technique, more precisely the clustering of the query workload. To validate our strategy, we implemented an XML warehouse modeled along the XCube specifications. We executed a workload of XQuery decision-support queries on this warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when queries are complex.