LAS: A dynamically adaptive database subsystem for better query processing performances

Nai Kuang Andrew Chen, Paulo B Goes, James R. Marsden

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For today's information markets and electronic commerce, timely information is crucial to competitive survival. Most of this past research has been focused on designing a single `optimal' database to process foreseeable queries or adopting optimization techniques for existing databases to get better query processing speed. These traditional techniques fall short in meeting the new challenges of today's dynamically changing environment and uncertain/dynamic query patterns. Our objective is to investigate and compare maintenance of multiple databases and/or materialized views for optimally answering specific queries and dynamically redesigning database structures to adapt changing query patterns. We propose that using a learning/assigning subsystem (LAS) detect the changing patterns of queries and then dynamically assign each query to the most suitable database structures and/or materialized views for processing. The learning/assigning subsystem (LAS) includes a learning engine, a knowledge base, and a trigger mechanism. The initial experimental results indicate that the LAS with eight database structures had much better query processing performance with faster response times than the traditional database system using any single database structure.

Original languageEnglish (US)
Title of host publicationProceedings - Annual Meeting of the Decision Sciences Institute
Editors Anon
PublisherDecis Sci Inst
Pages814
Number of pages1
Volume2
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3) - San Diego, CA, USA
Duration: Nov 22 1997Nov 25 1997

Other

OtherProceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3)
CitySan Diego, CA, USA
Period11/22/9711/25/97

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ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

Cite this

Chen, N. K. A., Goes, P. B., & Marsden, J. R. (1997). LAS: A dynamically adaptive database subsystem for better query processing performances. In Anon (Ed.), Proceedings - Annual Meeting of the Decision Sciences Institute (Vol. 2, pp. 814). Decis Sci Inst.