Application of Clustering Algorithm CLOPE to the Query Grouping Problem in the Field of Materialized View Maintenance

Kateryna Novokhatska, Oleksii Kungurtsev


In recent years, materialized views (MVs) are widely used to enhance the database performance by storing pre-calculated results of resource-intensive queries in the physical memory. In order to identify which queries may be potentially materialized, database transaction log for a long period of time should be analyzed. The goal of analysis is to distinguish resource-intensive and frequently used queries collected from database log, and optimize these queries by implementation of MVs. In order to achieve greater efficiency of MVs, they were used not only for the optimization of single queries, but also for entire groups of queries that are similar in syntax and execution results. Thus, the problem stated in this article is the development of approach that will allow forming groups of queries with similar syntax around the most resource-intensive queries in order to identify the list of potential candidates for materialization. For solving this problem, we have applied the algorithm of categorical data clustering to the query grouping problem on the step of database log analysis and searching candidates for materialization. In the current work CLOPE algorithm was modified to cover the introduced problem. Statistical and timing indicators were taken into account in order to form the clusters around the most resource intensive queries. Application of modified algorithm CLOPE allowed to decrease calculable complexity of clustering and to enhance the quality of formed groups.

ACM CCS (2012) Classification: Information systems→ Data management systems → Database management system engines→Query optimization;
Computing methodologies→Cluster analysis

*To cite this article: K. Novokhatska and O. Kungurtsev, "Application of Clustering Algorithm CLOPE to the Query Grouping Problem in the Field of Materialized View Maintenance", CIT. Journal of Computing and Information Technology, vol. 24, no. 1, pp. 79-89, 2016.


query, grouping, clustering, materialized view, CLOPE, categorical data

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