Abstract
Institutional databases can be instrumental in understanding a business process, but additional databases are also needed to broaden the empirical perspective on the investigation. We present a few data mining principles by which a business process can be analyzed in quantitative details and new process components can be postulated. Sequential and parallel process decomposition can apply, guided by human understanding of the investigated process and the results of data mining. In a repeated cycle, human operators formulate open questions, use queries to get relevant data, use quests that invoke automated search, and interpret the discovered knowledge. As an example we use mining for knowledge about student enrollment, which is an essential part of the university educational process. The target of discovery has been quantitative knowledge useful in understanding the university enrollment. Many discoveries have been made. The particularly surprising findings have been presented to the university administrators and affected the institutional policies.
Chapter PDF
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
Bhattacharyya, G.K., and Johnson, R.A. 1986. Statistical Concepts and Methods. Wiley: New York.
Buntine, W. 1996. Graphical Models for Discovering, Knowledge, in Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy eds. Advances in Knowledge Discovery and Data Mining, AAAI Press, p.59–82.
Chambers, S. & Sanjeev, A., 1997. Reflecting Metropolitan-Based Missions in Performance Indicator Reporting, Metropolitan Universities, Vol. 8, No. 3, p. 135–152.
Druzdzel, M., and Glymour, C. 1994. Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges. In Proc. of the AAAI-94 KDD Workshop, p. 419–430.
Heckerman, D. 1996. Bayesian Networks for Knowledge Discovery, in Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy eds. Advances in Knowledge Discovery and Data Mining, AAAI Press, p.59–82.
Klösgen, W. 1992. Patterns for Knowledge Discovery in Databases. In Proc. of the ML-92 Workshop on Machine Discovery, 1–10. National Institute for Aviation Research, Wichita, KS: Żytkow J. ed.
Ohrn, A.; Komorowski, J.; Skowron, A. & Synak, P. 1998. The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets—The ROSETTA System, To appear in Rough Sets in Knowledge Discovery, L. Polkowski and A. Skowron (eds.), Physica Verlag, 24 pages.
Piatetsky-Shapiro, G. and Matheus, C. 1991. Knowledge Discovery Workbench. In Proc. of AAAI-91 KDD Workshop, 11–24. Piatetsky-Shapiro G. ed.
Sanjeev, A., & Zytkow, J., 1996. A Study of Enrollment and Retentioo in a University Database, Journal of the Mid-America Association of Educational Opportunity Program Personnel, Vol. VIII, No. 1, Fall, p. 24–41.
Spirtes, P.; Glymour, C.; and Scheines, R. 1993. Causality, Statistics and Search. Ziarko, W. & Shan, N. 1994. KDD-R: A Comprehensive System for Knowledge Discovery in Databases Using Rough Sets, in T.Y. Lin & A.M. Wildberger eds. Proc. of Intern. Workshop on Rough Sets and Soft Computing, pp. 164–73.
Żytkow, J.; and Zembowicz, R. 1993. Database Exploration in Search of Regularities. Journal of Intelligent Information Systems 2:39–81.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sanjeev, A.P., Żytkow, J.M. (1998). Modeling the business process by mining multiple databases. In: Żytkow, J.M., Quafafou, M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0094847
Download citation
DOI: https://doi.org/10.1007/BFb0094847
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65068-3
Online ISBN: 978-3-540-49687-8
eBook Packages: Springer Book Archive