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CLASSIC: A Structural Data Model for Objects.

Alexander Borgida, Ronald J. Brachman, Deborah L. McGuinness, Lori Alperin Resnick: CLASSIC: A Structural Data Model for Objects. SIGMOD Conference 1989: 58-67
@inproceedings{DBLP:conf/sigmod/BorgidaBMR89,
  author    = {Alexander Borgida and
               Ronald J. Brachman and
               Deborah L. McGuinness and
               Lori Alperin Resnick},
  editor    = {James Clifford and
               Bruce G. Lindsay and
               David Maier},
  title     = {CLASSIC: A Structural Data Model for Objects},
  booktitle = {Proceedings of the 1989 ACM SIGMOD International Conference on
               Management of Data, Portland, Oregon, May 31 - June 2, 1989},
  publisher = {ACM Press},
  year      = {1989},
  pages     = {58-67},
  ee        = {http://doi.acm.org/10.1145/67544.66932, db/conf/sigmod/BorgidaBMR89.html},
  crossref  = {DBLP:conf/sigmod/89},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

CLASSIC is a data model that encourages the description of objects not only in terms of their relations to other known objects, but in terms of a level of intensional structure as well. The CLASSIC language of structured descriptions permits i) partial descriptions of individuals, under an 'open world' assumption, ii) answers to queries either as extensional lists of values or as descriptions that necessarily hold of all possible answers, and iii) an easily extensible schema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of defining and populating the DB, as well as querying and answering.

CLASSIC (for which we have a prototype main-memory implementation) can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects.

The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This space of languages differs from the subsets of predicate calculus hitherto explored by deductive databases.

Copyright © 1989 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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James Clifford, Bruce G. Lindsay, David Maier (Eds.): Proceedings of the 1989 ACM SIGMOD International Conference on Management of Data, Portland, Oregon, May 31 - June 2, 1989. ACM Press 1989 BibTeX , SIGMOD Record 18(2), June 1989
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Referenced by

  1. Vitaliy L. Khizder, David Toman, Grant E. Weddell: On Decidability and Complexity of Description Logics with Uniqueness Constraints. ICDT 2001: 54-67
  2. H. V. Jagadish, Laks V. S. Lakshmanan, Tova Milo, Divesh Srivastava, Dimitra Vista: Querying Network Directories. SIGMOD Conference 1999: 133-144
  3. Maurizio Lenzerini: Description Logics and Their Relationships with Databases. ICDT 1999: 32-38
  4. Domenico Beneventano, Sonia Bergamaschi, Stefano Lodi, Claudio Sartori: Consistency Checking in Complex Object Database Schemata with Integrity Constraints. IEEE Trans. Knowl. Data Eng. 10(4): 576-598(1998)
  5. Daniela Florescu, Alon Y. Levy, Alberto O. Mendelzon: Database Techniques for the World-Wide Web: A Survey. SIGMOD Record 27(3): 59-74(1998)
  6. Judith Bayard Cushing, Justin Laird, Emir Pasalic, Elizabeth Kutter, Tim Hunkapiller, Frank Zucker, David P. Yee: Beyond Interoperability - Tracking and Managing the Results of Computational Applications. SSDBM 1997: 223-236
  7. Richard Hull: Managing Semantic Heterogeneity in Databases: A Theoretical Perspective. PODS 1997: 51-61
  8. Vipul Kashyap, Amit P. Sheth: Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach. VLDB J. 5(4): 276-304(1996)
  9. Panos Constantopoulos, Matthias Jarke, John Mylopoulos, Yannis Vassiliou: The Software Information Base: A Server for Reuse. VLDB J. 4(1): 1-43(1995)
  10. Alexander Borgida: Description Logics in Data Management. IEEE Trans. Knowl. Data Eng. 7(5): 671-682(1995)
  11. Elisa Bertino, Paola Foscoli: Index Organizations for Object-Oriented Database Systems. IEEE Trans. Knowl. Data Eng. 7(2): 193-209(1995)
  12. Ashish Gupta, Inderpal Singh Mumick, Kenneth A. Ross: Adapting Materialized Views after Redefinitions. SIGMOD Conference 1995: 211-222
  13. Tanel Tammet: Using Resolution for Extending KL-ONE-type Languages. CIKM 1995: 326-332
  14. Serge Abiteboul, Richard Hull, Victor Vianu: Foundations of Databases. Addison-Wesley 1995, ISBN 0-201-53771-0
    Contents
  15. Howard W. Beck, Tarek M. Anwar, Shamkant B. Navathe: A Conceptual Clustering Algorithm for Database Schema Design. IEEE Trans. Knowl. Data Eng. 6(3): 396-411(1994)
  16. Carole A. Goble, Peter Crowther: Schemas for Telling Stories in Medical Records. EDBT 1994: 393-406
  17. Alexander Borgida, Ronald J. Brachman: Loading Data into Description Reasoners. SIGMOD Conference 1993: 217-226
  18. Sonia Bergamaschi, Claudio Sartori: On Taxonomic Reasoning in Conceptual Design. ACM Trans. Database Syst. 17(3): 385-422(1992)
  19. Domenico Beneventano, Sonia Bergamaschi: Subsumption for Complex Object Data Models. ICDT 1992: 357-375
  20. Michael Halper, James Geller, Yehoshua Perl: "Part" Relations for Object-Oriented Databases. ER 1992: 406-422
  21. Yves Caseau, Laurent Perron: A Type System for Object-Oriented Database Programming and Querying Languages. DBPL 1991: 218-235
  22. Michael V. Mannino, Injun Choi: Object-Oriented Modelling and Reasoning. ER 1990: 455-464
  23. Rakesh Agrawal, Alexander Borgida, H. V. Jagadish: Efficient Management of Transitive Relationships in Large Data and Knowledge Bases. SIGMOD Conference 1989: 253-262
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