Accommodating Exceptions in Databases, and Refining the Schema by Learning from them.

@inproceedings{DBLP:conf/vldb/BorgidaW85,
  author    = {Alexander Borgida and
               Keith E. Williamson},
  editor    = {Alain Pirotte and
               Yannis Vassiliou},
  title     = {Accommodating Exceptions in Databases, and Refining the Schema
               by Learning from them},
  booktitle = {VLDB'85, Proceedings of 11th International Conference on Very
               Large Data Bases, August 21-23, 1985, Stockholm, Sweden},
  publisher = {Morgan Kaufmann},
  year      = {1985},
  pages     = {72-81},
  ee        = {db/conf/vldb/BorgidaW85.html},
  crossref  = {DBLP:conf/vldb/85},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

To utilize DBMSs, a database designer must usually construct a schema, which is used to validate the data stored and help set up efficient access structures. Because database design is an art, and because the real world is irregular, unpredictable, and evolves, truly useful database systems must be tolerant of occasional deviations from the constraints imposed by the schema, including the semantic integrity constraints. We therefore examine the problems involved in accommodating ezceptional information in a database, and outline techniques for resolving them.

Furthermore, we consider ways in which the schema can be refined to better characterize reality as it is reflected in the data encountered, including the exceptions. For this purpose, we describe part of a "database administrator's assistant" - a computer system which can suggest modifications and additions to the current definitions and integrity constraints in the schema. This system makes generalizations from the currently encountered exceptions, and is based on techniques used in Machine Learning.

Copyright © 1985 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Referenced by

1. Qing Li, Dennis McLeod: Conceptual Database Evolution Through Learning in Object Databases. IEEE Trans. Knowl. Data Eng. 6(2): 205-224(1994)
2. Christian Esculier: Non-Monotonic Knowledge Evolution in VLKDBs. VLDB 1990: 638-649
3. Howard M. Dreizen, Shi-Kuo Chang: Imprecise Schema: A Rationale for Relations with Embedded Subrelations. ACM Trans. Database Syst. 14(4): 447-479(1989)
4. Michel E. Adiba, Christine Collet: Management of Complex Objects as Dynamic Forms. VLDB 1988: 134-147
5. Malcolm P. Atkinson, Peter Buneman: Types and Persistence in Database Programming Languages. ACM Comput. Surv. 19(2): 105-190(1987)
6. Heikki Mannila, Kari-Jouko Räihä: Dependency Inference. VLDB 1987: 155-158