How does knowledge discovery cooperate with active database techniques in controlling dynamic environment?

  • Hiroyuki Kawano
  • Shojiro Nishio
  • Jiawei Han
  • Toshiharu Hasegawa
Active Databases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 856)


A dynamic environment, such as a production process, a communication network, highway traffic, etc., may contain a huge amount of information, changing with time, which is a valuable resource for understanding the general behavior of the environment, discovering the regularities and anomalies currently happening in the environment, controlling an evolution process, and intelligent modeling or managing 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 study, a knowledge discovery technique is integrated with data sampling and active database techniques 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.


Knowledge Discovery Dynamic Environment Active Rule Evolution Rule Conditional Evaluator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Hiroyuki Kawano
    • 1
  • Shojiro Nishio
    • 2
  • Jiawei Han
    • 3
  • Toshiharu Hasegawa
    • 1
  1. 1.Department of Applied Math, and PhysicsKyoto UniversityKyotoJapan
  2. 2.Department of Information Systems EngineeringOsaka UniversityOsakaJapan
  3. 3.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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