An Auto-Indexing Technique for Databases Based on Clustering

Authors:
Mujiba Zaman;Jyotsna Surabattula;Le Gruenwald
Affiliations:
The University of Oklahoma, Norman, OK;The University of Oklahoma, Norman, OK;The University of Oklahoma, Norman, OK
Venue:
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Year:
2004

Citing 0
Cited 2

Research issues in automatic database clustering

ACM SIGMOD Record
Data mining-based materialized view and index selection in data warehouses

Journal of Intelligent Information Systems

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Abstract

Considering the wide deployment of databases and its size, particularly in data warehouses, it is important to automate the physical design so that the task of the database administrator (DBA) is minimized. An important part of physical database design is index selection. An auto-index selection tool capable of analyzing large amounts of data and suggesting a good set of indexes for a database is the goal of auto-administration. Clustering is a data mining technique with broad appeal and usefulness in exploratory data analysis. This idea provides a motivation to apply clustering techniques to obtain good indexes for a workload in the database. In this paper we describe a technique for auto-indexing using clustering. The experiments conducted show that the proposed technique performs better than Microsoft SQL Server Index Selection Tool (IST) and can suggest indexes faster than Microsoft's IST.