Knowledge Discovery and Mandgement for Information Network

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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 ModelsWe 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 NetworkWe 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.