article

Free access

Conceptual learning in database design

Editor: Robert B. Allen Authors:

Yannis E. Ionnidis,

Andrew J. WhitsittAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 10, Issue 3

Pages 265 - 293

https://doi.org/10.1145/146760.146779

Published: 01 July 1992 Publication History

Abstract

This paper examines the idea of incorporating machine learning algorithms into a database system for monitoring its stream of incoming queries and generating hierarchies with the most important concepts expressed in those queries. The goal is for these hierarchies to provide valuable input to the database administrator for dynamically modifying the physical and external schemas of a database for improved system performance and user productivity. The criteria for choosing the appropriate learning algorithms are analyzed, and based on them, two such algorithms, UNIMEM and COBWEB, are selected as the most suitable ones for the task. Standard UNIMEM and COBWEB implementations have been modified to support queries as input. Based on the results of experiments with these modified implementations, the whole approach appears to be quite promising, expecially if the concept hierarchy from which the learning algorithms start their processing is initialized with some of the most obvious concepts captured in the database.

References

[1]

ASTRAHAN, M. M. BLASGEN, M. W., CHAMBERLIN, D. D., ESWARAN~ K. P., GRAY, J. N., GRIFFITHS, P. D., KING, W. F., LORIE, R. A., MCJONES, P. R., MEHL, J. W., PUTZOLU, G. R., TRAIGER, I. L.~ WADE, B. W., AND WATSON, V. System R: A relational approach to data management. ACM Trans. Patobase Syst. 1, 2 (June 1976), 97-137.

[2]

BISWAS, G., WEINBERG, J., YANG, Q, AND KOLLER, G. Conceptual clustering and exploratory data analysis. In t>roceeclgngs of $he 8th International Workshop on Machine Learning (Evanston, I1, June 1991), Morgan Kaufmann, Los Altos, Calif., 1991, pp. 591-595.

[3]

BORGIDA, A., AND WILLIAMSON, K. E. Accommodating exceptions in database, and refining the schema by learning from them. In Proceedings of the 11th I~ternattonal VLDB Conference (Stockholm, Aug. 1985), Morgan Kaufmann, Los Altos, Calif., pp. 72-81.

[4]

FISHER, D.H. Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2, 2 (1987), 139-172.

[5]

GENNARI, J.H. Focused concept formation. In Proceedings of the 6th International Workshop on Machine Learning (Ithica, N.Y., June 1989), Morgan Kaufmann, Los Altos, Calif., 1989. pp. 379 382.

[6]

GENNARI, J. H., LANGLEY, P., AND FISHER, D. Models of incremental concept formation. Artif. lntell. 40, 1-3 (Sept. 1989), 11-61.

[7]

HINTEREERG, H., NIEVERGELT, J., AND SEVCIK, K.C. The grid file: An adaptable, symmetric muttikey file structure. ACM Trans. Database Syst. 9, i (Mar. 1984), 38-71.

[8]

KOLODNER, J.L. Reconstructive memory: A computer model. Cognitive Sci. 7, 4 (Oct.-Dec. 1983), 281 328.

[9]

LANGLEY, P., THOMPSON, K., IBA, W., GENNARI, J. H., AND ALLEN, J. A. An integrated cognitive architecture for autonomous agents. Teeh. Rep. 89-28, Univ. of Calif., Irvine, Sept. 1989.

[10]

LEBOWlTZ, M. Concept learning in a rich input domain: Generalization-based memory. In Machine Learning: An Artificial Intelligence Approach, Vol. II. J. G. Carbonell, R. S. Michalski and T. M. Mitchell, Eds., Morgan Kaufmann, Los Altos, Calif., 1987, pp. 193-214.

[11]

LEBOWlTZ, M. Experiments with incremental concept formation: UNIMEM. Mach. Learn. 2, 2 (1987), 103 138.

[12]

LI, Q., AND MCLEOD, D. Object flavor evolution through learning in an object-oriented database system. In Expert Database Systems, Proceedings from the Second International Conference, L., Kerschberg, Ed., Benjamin/Cummings, Menlo Park, Calif., 1989, pp. 469-495.

[13]

MITCHELL, T.M. Generalization as search. Art. Intell. 18, 2 (1982), 203-226.

[14]

MOTRO, A. Query generalization: A method for interpreting null answers. In Expert Database Systems, Proceedings from the First International Workshop, L. Kerschberg, Ed., Benjamin/Cummings, Menlo Park, Calif., 1986, pp. 597-616.

[15]

MOTRO, A. VAGUE: A user interface to relational databases that permits vague queries. ACM Trans. Off. Inf. Syst. 6, 3 (1988), 187 214.

[16]

QUINLAN, J.R. Induction of decision trees. Mach. Learn. 1, I (1986), 81-106.

[17]

STEPP, R. E., AND MICHALSKI, R.S. Conceptual clustering: Inventing goal-oriented classifications of structured objects. In Machine Learning: An Artificial Intelligence Approach. Vol. H, J. G. Carbonell, R. S. Michalski and T. M. Mitchell, Eds., Morgan Kaufmann, Los Altos, Calif., 1987, pp. 472 498.

[18]

VALDURIEZ, P. Join ind~ces. ACM Trans. Database Syst. 12, 2 (June 1987), 218 246.

Cited By

Murakoshi KFujikawa S(2016)Growing Hierarchical Self-Organizing Map Using Category UtilityInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819401650010826:02(217-237)Online publication date: Mar-2016
https://doi.org/10.1142/S0218194016500108
Vobugari SSomayajulu DSubraya B(2012)A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)International Journal of Machine Learning and Computing10.7763/IJMLC.2012.V2.170(471-475)Online publication date: 2012
https://doi.org/10.7763/IJMLC.2012.V2.170
Al-Omary AJamil M(2006)A new approach of clustering based machine-learning algorithmKnowledge-Based Systems10.1016/j.knosys.2005.10.01119:4(248-258)Online publication date: 1-Aug-2006
https://dl.acm.org/doi/10.1016/j.knosys.2005.10.011
Show More Cited By

Index Terms

Conceptual learning in database design
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Data management systems

Recommendations

Conceptual Database Evolution Through Learning in Object Databases

Changes to the conceptual structure (meta-data) of a database are common in many application environments and are in general inadequately supported by existing database systems. An approach to supporting such meta-data evolution in a simple, extensible, ...
Learning Concept Lengths Accelerates Concept Learning in ALC
The Semantic Web
Abstract
Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individuals. However, the number of concepts explored by these approaches ...
Incremental Learning from Noisy Data

Induction of a concept description given noisy instances is difficult and is further exacerbated when the concepts may change over time. This paper presents a solution which has been guided by psychological and mathematical results. The method is based ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 10, Issue 3

July 1992

103 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/146760

Editor:
Robert B. Allen
Bellcore, Morristown, NJ

Issue’s Table of Contents

Copyright © 1992 ACM.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1992

Published in TOIS Volume 10, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
680
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)9

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Murakoshi KFujikawa S(2016)Growing Hierarchical Self-Organizing Map Using Category UtilityInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819401650010826:02(217-237)Online publication date: Mar-2016
https://doi.org/10.1142/S0218194016500108
Vobugari SSomayajulu DSubraya B(2012)A Model for Building Dynamic Indexes & Storage and Re-use of Optimal Query Plans Generated thru Progressive Optimization (POP)International Journal of Machine Learning and Computing10.7763/IJMLC.2012.V2.170(471-475)Online publication date: 2012
https://doi.org/10.7763/IJMLC.2012.V2.170
Al-Omary AJamil M(2006)A new approach of clustering based machine-learning algorithmKnowledge-Based Systems10.1016/j.knosys.2005.10.01119:4(248-258)Online publication date: 1-Aug-2006
https://dl.acm.org/doi/10.1016/j.knosys.2005.10.011
Al-Mathami S(1998)Knowledge Discovery in DatabasesJournal of King Saud University - Computer and Information Sciences10.1016/S1319-1578(98)80002-510(15-25)Online publication date: 1-Jan-1998
https://dl.acm.org/doi/10.1016/S1319-1578%2898%2980002-5
Chen HChung YRamsey MYang C(1998)A smart itsy bitsy spider for the WebJournal of the American Society for Information Science10.1002/(SICI)1097-4571(19980515)49:7<604::AID-ASI3>3.0.CO;2-T49:7(604-618)Online publication date: 15-May-1998
https://doi.org/10.1002/(SICI)1097-4571(19980515)49:7<604::AID-ASI3>3.0.CO;2-T
Mathkour HAl-Salamah A(1995)A machine learning technique for monitoring database systemsProceedings IEEE Symposium on Computers and Communications10.1109/SCAC.1995.523697(421-427)Online publication date: 1995
https://doi.org/10.1109/SCAC.1995.523697
Chu WChiang K(1994)Abstraction of high level concepts from numerical values in databasesProceedings of the 3rd International Conference on Knowledge Discovery and Data Mining10.5555/3000850.3000865(133-144)Online publication date: 31-Jul-1994
https://dl.acm.org/doi/10.5555/3000850.3000865
Godin RMissaoui R(1994)An incremental concept formation approach for learning from databasesTheoretical Computer Science10.1016/0304-3975(94)90195-3133:2(387-419)Online publication date: 24-Oct-1994
https://dl.acm.org/doi/10.1016/0304-3975%2894%2990195-3
Missaoui RGodin RBoujenoui A(1994)Extracting Exact and Approximate Rules from DatabasesIncompleteness and Uncertainty in Information Systems10.1007/978-1-4471-3242-4_16(209-222)Online publication date: 1994
https://doi.org/10.1007/978-1-4471-3242-4_16
Azarbod CPerrizo W(1993)Building concept hierarchies for schema integration in HDDBS using incremental concept formationProceedings of the second international conference on Information and knowledge management10.1145/170088.170480(732-734)Online publication date: 1-Dec-1993
https://dl.acm.org/doi/10.1145/170088.170480
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents