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Learning and Classification in the Extensional Object Model  

Kim, Yong-Jae (College of Business Administration, Konkuk University)
An, Joon-M. (College of Business Administration, Konkuk University)
Lee, Seok-Jun (College of Business Administration, Konkuk University)
Publication Information
Asia pacific journal of information systems / v.17, no.1, 2007 , pp. 33-58 More about this Journal
Abstract
Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.
Keywords
Object-oriented Databases; Machine Learning; Query Language; Inheritance; Classification;
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1 Clark, P. and Niblett, T. 'The CN2 Algorithm,' Machine Learning, Vol. 6, No. 4, 1989, pp. 261-283
2 Creecy, R., Masand, B., Smith, S., and Waltz, D. 'Trading MIPS and Memory for Knowledge Engineering,' Communications of the ACM, Vol. 35, No. 8, August 1992, pp. 48-64
3 Frawley, W., Piatetsky-Shapiro, G., and Matheus, C. 'Knowledge Discovery in Databases: An Overview,' in Proc. First International Conference on Knowledge Discovery and Databases, October 1991, New York
4 Li, Q. and McCleod, D. 'Object Flavor Evolution Through Learning in an Object-Oriented Database System,' in Proc. of the 2nd International Conference on Expert Database Systems, L. Kerschberg (ed.), 1989, Benjamin Cummings, Menlo Park, CA, pp. 469-495
5 Matheus, C.J., Chan, P.K., and Piatetsky-Shapiro, G., 'Systems for Knowledge Discovery in Databases,' IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, December 1993, pp. 903-913   DOI   ScienceOn
6 McCann, J. and Gallagher, J. Expert Systems for Scanner Data Environments, International Series in Quantitative Marketing, Kluwer Academic Publishers, 1990
7 Orton, J. and Weick, K. 'Loosely Coupled Systems: A Reconceptualization,' Academy of Management Review, Vol. 15, No. 2, 1990
8 Quinlan, J. 'Induction of Decision Trees,' Machine Learning, Vol. 1, 1986, pp. 81-106
9 Aha, D., Kibler, D. ani Albert, M 'Instance-Based Learning Algorithms,' Machine Learning, Vol. 6, 1991, pp. 37-66
10 DL86 Daft, R. and Lengel, R. 'Organizational Information Requirements, Media Richness and Structural Design,' Management Science, Vol. 32, No. 5, 1986
11 Lieberman, H. 'Using Prototypical Objects to Implement Shared Behavior in Object Oriented Systems,' in Proc. OOPSLA Conference, October 1986
12 Kim, W. 'Object-Oriented Databases: Definition and Research Directions,' IEEE Transactions on Knowledge and Data Engineering, Vol. 2, No. 3, September 1990
13 Mannino, M., Choi, I., and Batory, D.'The Object-Oriented Functional Data Lanaguage,' IEEE Transactions on Software Engineering, Vol. 16, No. 11, November 1990, pp. 1258-1272   DOI   ScienceOn
14 Jung, C. A Framework for Computer-Supported Interpretation Systems, Ph.D. Dissertation, The University of Texas at Austin, Department of Management Science and Information Systems, May 1992
15 Lalonde, W., Thomas, D., and Pugh, D. 'An Exemplar Based Smalltalk,' in Proc. OOPSLA Conference, October 1986
16 Hansen, E. and Widom, J. 'Rule Processing in Active Database Systems,' in Advances in Database and Artificial Intelligence, JAI Press, Greenwich, Connecticut, 1992
17 Daft, R. and Weick, C. 'Towards a Model of Organizations as Interpretation Systems,' Academy of Management Review, Vol. 9, No. 2, 1984
18 Utgoff, P. 'Incremental Induction of Decision Trees,' Machine Learning, Vol. 4, 1989, Kluwer Academic Publishers, pp. 161-186   DOI
19 Gennari, J., Langley, P., and Fisher, D. 'Models of Incremental Concept Formation,' Artificial Intelligence, Vol. 40, 1989, pp. 11-61   DOI   ScienceOn
20 Smyth, P. and Goodman, R. 'An Information Theoretic Approach to Rule Induction from Databases,' IEEE Transactions on Knowledge and Data Engineering, Vol. 4, No. 4 (August 1992), pp. 301-316   DOI   ScienceOn
21 Rao, Raghav and An, Joon M., 'The effect of team composition on decision scheme, information search, and perceived complexity,' Journal of Organizational Computing and Electronic Commerce, 1995, Vol. 5 Issue 1, pp. 1-20   DOI   ScienceOn
22 Clark, P. and Boswell, R. 'Rule Induction with CN2: Some Recent Improvements,' in Proc. Machine Learning - European Working Session on Learning, Porto, Portugal, Springer-Verlag, March 1991, pp. 151-163
23 Ceri, S. and Widom, J. 'Deriving Production Rules for Constraint Maintenance,' in Proc. of the Sixteenth International Conf. on Very Large Data Bases, Brisbane, Australia, August 1990, pp. 566-577
24 Borgida, A., Brachman, R., McGuinness, D., and Resnick, L. 'CLASSIC: A Structural Data Model for Objects,' in Proc. ACM SIGMOD Conference, May 1989, Portland
25 Fayyad, U., Piatetsky-Shapiro, G. and Smyth P., 'From Data Mining to Knowledge Discovery in Databases,' AI Magazine, Fall 1996, pp. 37-54
26 Goebel M., Le Gruenwald. 'A Survey of Data Mining and Knowledge Discovery Software Tools,' ACM SIGKDD Explorations Newsletter, Vol. 1, 1, 1999, pp. 1-20   DOI
27 Anthony, M. and Biggs, N. Computational Learning Theory, Cambridge University Press, 1992
28 Ioannidis, Y., Saulys, T., D. and Witsitt, A. 'Conceptual Learning in Database Design,' ACM Transactions on Information Systems, Vol. 10, No. 3, July 1992, pp. 265-294   DOI
29 Borgida, A. and Williamson, K. 'Accommodating Exceptions in Databases and Refining the Schema by Learning from Them,' in Proc. of the 11th International VLDB Conference, August 1985, Stockholm, pp. 72-81
30 Sciore, E. 'Object Specialization,' ACM Transactions on Information Systems, Vol. 7, No. 2, 1989