Abstract
The use of database management system integrity constraints has been a powerful tool in raising the quality of data within an application subject database. Unfortunately, the successful use of integrity constraints requires that the database administrator has implemented the constraint before data are inserted into the database.
The results of this research provide a methodology for discovering previously unknown integrity relationships in a relational database. The methodology uses the principles of knowledge discovery from the artificial intelligence community, and Quinlan's ID3 machine learning algorithm as the discovery tool. Experimental results are provided that demonstrate how the methodology can be applied.
Recommended Citation
Harrier, D. and St. Clair, D. C., "Discovery of Integrity Relationships in Relational Databases" (1995). Computer Science Technical Reports. 182.
https://scholarsmine.mst.edu/comsci_techreports/182
Department(s)
Computer Science
Second Department
Mathematics and Statistics
Report Number
CSc-95-04
Document Type
Technical Report
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1995 University of Missouri - Rolla, All rights reserved
Publication Date
01 May, 1995
Comments
The first Author is a Graduatee Student
This report is primarily the M.S. thesis of the first author, completed May 1995.
Permission to copy without fee all or part of the material is granted provided that the copies are not made or distributed for direct commercial advantage. To copy otherwise, or to republish, requires a fee and/or specific permission.
This thesis has been prepared in accordance with the format used by the Association of Computing Machinery (ACM). Pages 1-32 will be presented for publication in the ACM journal SIGMOD RECORD. The Appendix has been added for purposes normal for thesis writing.