Abstract
Modeling of KDD (Knowledge Discovery in Databases) process constitutes an important and new research area of KDD, that is, meta levels of the KDD process, including formal specification of the process, its planning, scheduling, controlling, management, evolution, and reuse. The key issue is how to increase both autonomy and versatility of a KDD system. Our methodology is to create an organized society of KDD agents. This means (1) to develop many kinds of KDD agents for different discovery tasks; (2) to use the KDD agents in multiple learning phases in a distributed cooperative mode; (3) to manage the society of KDD agents by multiple meta-control levels. Based on this methodology, a multi-strategy and cooperative KDD system, which can be imagined as a softbot and is named GLS (Global Learning Scheme), has being developing by us. This paper focuses on the meta control levels for increasing both autonomy and versatility of the KDD system.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Brachman, R.J. and Anand, T. 1996. The Process of Knowledge Discovery in Databases: A Human-Centred Approach. In Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, 37–58.
Durfee, E.H. and Lesser, V.R. 1989. Negotiating Task Decomposition and Allocation using Partial Global Planning. Distributed Artificial Intelligence Vol. 2.
Fayyad, U.M., Piatetsky-Shapiro, G et al (eds.) 1996. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press.
Liu, C. 1991. Software Process Planning and Execution: Coupling vs. Integration. LNCS 498, Springer Verlag, 356–374.
Liu, C. and Conradi, R. 1993. Automatic Replanning of Task Networks for Process Evolution in EPOS. Proc. the 4th European Software Engineering Conference (ESEC'93), LNCS 717, Springer Verlag, pp. 437–450.
Matheus, C.J., Chan, P.K. & Piatetsky-Shapiro, G. 1993. Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng., 5(6):904–913.
Michalski, R.S. et al. 1992. Mining for Knowledge in Databases: The INLEN Architecture, Initial Implementation and First Results. J. of Intell. Infor. Sys., KAP, 1(1):85–113.
Minsky, M. 1986. The Society of Mind, Simon and Schuster, New York.
Ohsuga, S. 1970. On the Value of Information and Decision Making. Trans. of Information Processing of Japan, Vol. 10:97–108.
Ohsuga, S. 1990. Framework of Knowledge Based Systems. Knowledge Based Systems, 3(4):204–214.
Ohsuga, S. 1995. A Way of Designing Knowledge Based Systems. Knowledge Based Systems, 8(4):211–222.
Piatetsky-Shapiro, G. & Frawley, W.J. (eds.). 1991. Knowledge Discovery in Databases. AAAI/MIT Press.
Zhang, X. 1996. Co-scheduling Parallel Workloads across Networks of Workstations, invited talk at Yamaguchi Univ. Japan, June 1996.
Zhong, N. & Ohsuga, S. 1994a. Discovering Concept Clusters by Decomposing Databases. Data & Knowl. Eng., Elsevier Science Publishers, 12(2):223–244.
Zhong, N. & Ohsuga, S. 1994b. The GLS Discovery System: Its Goal, Architecture and Current Results. Proc. 8th Inter. Symp. on Methodologies for Intell. Sys. (ISMIS'94). LNAI 869, Springer, 233–244.
Zhong, N. & Ohsuga, S. 1995a. KOSI — An Integrated System for Discovering Functional Relations from Databases. J. of Intell. Infor. Sys., KAP, 5(1):20–50.
Zhong, N. and Ohsuga, S. 1995b. Toward A Multi-Strategy and Cooperative Discovery System. Proc First Inter. Conf. on Knowledge Discovery and Data Mining (KDD-95), AAAI Press, 337–342.
Zhong, N. and Ohsuga, S. 1996a. System for Managing and Refining Structural Characteristics Discovered from Databases. Knowledge Based Systems, Elsevier, 9(4):267–279.
Zhong. N. and Ohsuga. S. 1996b. A Hierarchical Model Learning Approach for Refining and Managing Concept Clusters Discovered from Databases. Data & Knowl. Eng., Elsevier Science Publishers, 20(2): 227–252.
Zhong, N. and Ohsuga, S. 1996c. Using Generalizations Distribution Tables as a Hypothesis Search Space for Generalization. Proc. 4th Inter. Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD'96) (1996) 396–403.
Zhong, N., Fujitsu, S., and Ohsuga, S. 1997. Generalization Based on the Connectionist Networks Representation of a Generalization Distribution Table, H. Lu, et al. (eds.) KDD: TECHNIQUES AND APPLICATIONS. Proc. First Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-97), World Scientific (1997) 183–197.
Zytkow, J.M. 1993. Introduction: Cognitive Autonomy in Machine Discovery. Machine Learning, KAP, 12(1–3):7–16.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhong, N., Ohsuga, S., Liu, C., Kakemoto, Y., Zhang, X. (1997). On meta levels of an organized society of KDD agents. In: Komorowski, J., Zytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1997. Lecture Notes in Computer Science, vol 1263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63223-9_136
Download citation
DOI: https://doi.org/10.1007/3-540-63223-9_136
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63223-8
Online ISBN: 978-3-540-69236-2
eBook Packages: Springer Book Archive