An Adaptive Materialized Query Selection Method in a Mediator System


The KIPS Transactions:PartD, Vol. 11, No. 1, pp. 83-94, Feb. 2004
10.3745/KIPSTD.2004.11.1.83,   PDF Download:

Abstract

Recent researches which purport to integrate distributed information have been concentrated on developing efficient mediators systems that not only provide a high degree of autonomy for local users but also support the flexible integration of required functions for global users. However, there has been little attention on how to evaluate a global query in a mediator. A global query is transformed into a set of its sub-queries and each sub-query is the unit of evaluation in a remote server. Therefore, it is possible to speed up the execution of a global query if the previous results of frequently evaluated sub-queries are materialized in a mediator. Since the integration schema of a mediator can be incrementally modified and the evaluation frequency of a global query can also be continuously varied, query usage should be carefully monitored to determine the optimized set of materialized sub-queries. Furthermore, as the number of sub-queries increases, the optimization process itself may take too long, so that the optimized set identified by a long optimization process may become obsolute dueto the recent change of query usage. This paper proposes the adaptive selection of materialized sub-queries such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a query from the past, the accumulated usage frequency of a query decays as time goes by.


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Cite this article
[IEEE Style]
J. G. Hong and L. W. Seog, "An Adaptive Materialized Query Selection Method in a Mediator System," The KIPS Transactions:PartD, vol. 11, no. 1, pp. 83-94, 2004. DOI: 10.3745/KIPSTD.2004.11.1.83.

[ACM Style]
Ju Gil Hong and Lee Won Seog. 2004. An Adaptive Materialized Query Selection Method in a Mediator System. The KIPS Transactions:PartD, 11, 1, (2004), 83-94. DOI: 10.3745/KIPSTD.2004.11.1.83.