知識発見とアクティブデータベース技術の融合

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Information flow in a dynamic environment, such as a production process and a communication network, is a valuable resource for understanding the general behavior of the environment, discovering the regularities and anomalies currently happening in the environment, controlling the evolution process, and modeling and intelligent management of the environment. Unfortunately, the data generated in a dynamic environment are often expressed in low level primitives and in huge volumes. Because of the dynamic, continuous and rapid changes of the information flow, it is difficult to catch the regularities and anomalies in a dynamic environment and react promptly for real-time applications. In this paper, a knowledge discovery technique is integrated with data sampling and active database techniques in order to discover interesting behaviors of a dynamic environment and react intelligently to the environment changes. The discovery of the dynamics in a computer communication network and the application of the discovered knowledge for network management are taken as an example in our study. The study shows (1) data sampling is necessary in the collection of information for regularity analysis and anomaly detection; (2) knowledge discovery is important for generalizing low level data to high-level information and detecting interesting patterns; (3) active database technology is essential for real-time reaction to the changes in a dynamic environment; and (4) an integration of the three technologies forms a powerful tool for control and management of large dynamic environments in many applications.
社団法人人工知能学会の論文
1996-09-01

知識発見とアクティブデータベース技術の融合

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