The Integration of Expert System and Database System

碩士 === 國立臺灣科技大學 === 工程技術研究所 === 81 === Integration of expert systems and database systems promises to make expert system work efficiently when it needs a very large knowledge base, while endowing database systems with inferencing capability...

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Bibliographic Details
Main Authors: Lung-Yu Chen, 陳隆裕
Other Authors: Chiu Hua Chen
Format: Others
Language:zh-TW
Published: 1993
Online Access:http://ndltd.ncl.edu.tw/handle/44722688883355468527
Description
Summary:碩士 === 國立臺灣科技大學 === 工程技術研究所 === 81 === Integration of expert systems and database systems promises to make expert system work efficiently when it needs a very large knowledge base, while endowing database systems with inferencing capability. We designed and implemented a system, called TIGER, that is a total integration of expert systems and database systems. In our system, all the facts of in traditional expert systems are stored in the relational database component of TIGER. All partial matchs, intermediate facts, and rule instantiations are also stored in the temporary database. To execute the rules of expert systems, we chose the TREAT match algorithm, appropriately modified, as a basis for rule matching because of its superior efficiency in comparison with other match algorithms and its suitability for integration with a DBMS. However, traditional rule execution strategy is sequential, resulting in extremely high frequency of disk access, and low overall system performance. We propose a novel and vital idea in our system to siginifcantly reduce the number of joins by applying parallel rule firing theory to rule execution in expert database systems. The intuition behind the idea is that when multiple rules are discovered to be firable in parallel, their joins can be performed at the same time, instead of being done separately, as traditional sequential execution of rules demands. Our experimental results confirms our intuition and demonstrates that as more parallel rules are present in the application, and as the data set gets larger, the system achieves very impresessive performance.