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A Decision Support System For Database Evolution Using Data Model Independent Architecture

Scope and Purpose

This paper presents methods for the control of large-scale databases--the repository of information pertaining to an enterprise using computers. In particular, the paper proposes a set of quantitative models for monitoring the performance of database systems when the changing nature of applications of the system is explicitly considered. Using these models, the systemÕs performance will be assessed constantly according to the criteria set for them. Also, decision rules will be derived to determine the optimal policies of control. The methods that are proposed in the paper are especially useful for operating settings where both the interrelationships of data and the uses envisioned for them are evolving rapidly. Some database constructs unavailable previously are developed to facilitate the implementation of such a decision framework for database systems.

Abstract

The existing corpus of scientific knowledge in the field of database systems (DBS) has been concerned mainly with such problems as database technologies and system development methodologies. Relatively few efforts have been devoted to the problem of adapting an ongoing DBS in a systematic fashion. Notwithstanding the lack of sufficient prior knowledge, this adaptation problem is critical in DBS management, since a DBS should really be conceived as an evolving structure, rather than a stable one-shot phenomenon. Toward this end, this paper proposes a reliability-based decision support framework for evolving a DBS systematically. Both user satisfaction with the DBS and the usage pattern of it are monitored on a real-time basis and used for controlling it adaptively. Chance-constrained models are proposed to characterize suitable decision rules for DBS evolution decisions ranging from file reorganization to DBS, whereby facilitating the control and evolution of DBS.

 

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