1994 Fiscal Year Final Research Report Summary
Knowledge Discovery and Mandgement for Information Network
| Project/Area Number |
05680297
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
HASEGAWA Toshiharu Kyoto Univ., Dept.of Applied Math.and Physics, Professor, 工学部, 教授 (40025911)
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| Co-Investigator(Kenkyū-buntansha) |
KAWANO Hiroyuki Kyoto Univ., Dept.of Applied Math.and Physics, Assistant Prof., 工学部, 助手 (70224813)
TAKINE Tetsuya Osaka Univ., Dept.of Information System, Assoc.Prof., 工学部, 助教授 (00216821)
TAKAHASHI Yutaka Kyoto Univ., Dept.of Applied Math.and Physics, Assoc.Prof., 工学部, 助教授 (00135526)
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| Project Period (FY) |
1993 – 1994
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| Keywords | Information System / Communication Network / Knowledge Discovery / Database / Performance Evaluation / Distributed System / Queneiry Theory / Knowledge Base |
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| Research Abstract |
In scientific and business applications, such as a information system, a communication network, a production process, etc., huge volume of data is generated rapidly and continuously, and stored into management database. In order to control such a synamic system, it is important to make mathematical model for analysis and to discover the useful knowledge from the law data.
Our project focused on the following points.
1.Hybrid Performance Evaluation System for Mathematical Models
We propose a model description language which is based on queneing theory. We developed a prototype of integrated software environment using object oriented language. Based on our developing konwledge base, our software environment can provide very reliable, flexible and user-friendly means for the performance evaluation purpose. Analyzer and simulator work in cooperation with each other on distributed environment.
2.Knowledge Discovery from Infomation Network
We can discover the form of functional or multi-valued dependency rules or integrity constraints in terms of primitive data from the resource of information network. We extended the attribute-oriented algorithm to make the bridge between the low-level processing data and high level understandable control primitives. Discovered knowledge by this induction algorithm can be also associated with statistical information. Our proposed algorithm discovers three kinds of interesting rules : characteristic rule, stable rule and evolution rule. The technology of active database offers the prompt, real-time, and intelligent management. It could substantially enhance the power of data mining in information network.
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