An Information-Theoretic Study on Aggregate Responses.

Chung-Dak Shum, Richard R. Muntz: An Information-Theoretic Study on Aggregate Responses. VLDB 1988: 479-490
  author    = {Chung-Dak Shum and
               Richard R. Muntz},
  editor    = {Fran\c{c}ois Bancilhon and
               David J. DeWitt},
  title     = {An Information-Theoretic Study on Aggregate Responses},
  booktitle = {Fourteenth International Conference on Very Large Data Bases,
               August 29 - September 1, 1988, Los Angeles, California, USA,
  publisher = {Morgan Kaufmann},
  year      = {1988},
  isbn      = {0-934613-75-3},
  pages     = {479-490},
  ee        = {db/conf/vldb/ShumM88.html},
  crossref  = {DBLP:conf/vldb/88},
  bibsource = {DBLP,}


An enumeration of individual objects is not always the best means of information exchange. This paper concerns the problem of providing aggregate responses to database queries. An aggregate response is an expression whose terms are quantified concepts. The tradeoff between the conciseness and preciseness of an aggregate response is studied. Conciseness is measured by the length (the number of terms) of an expression, and preciseness is measured by the entropy or the amount of uncertainty associated with the expression. For a given length, an expression with the minimum amount of entropy is called optimal. Under a one-level taxonomy with the same cardinalities for all leaf concepts, the problem of tinding an optimal expression can be solved inexpensively. An efficient heuristic is also proposed for the general one-level taxonomy. For a taxonomy of more than one level, an efficient heuristic is suggested which experiments indicate yields good solutions.

Copyright © 1988 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.

Online Paper

ACM SIGMOD Anthology

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Printed Edition

François Bancilhon, David J. DeWitt (Eds.): Fourteenth International Conference on Very Large Data Bases, August 29 - September 1, 1988, Los Angeles, California, USA, Proceedings. Morgan Kaufmann 1988, ISBN 0-934613-75-3


Tomasz Imielinski: Intelligent Query Answering in Rule Based Systems. J. Log. Program. 4(3): 229-257(1987) BibTeX
Francisco Corella: Semantic Retrieval and Levels of Abstraction. Expert Database Workshop 1984: 91-114 BibTeX
Chung-Dak Shum, Richard R. Muntz: Implicit Representation for Extensional Answers. Expert Database Conf. 1988: 497-522 BibTeX
Gultekin Özsoyoglu, Z. Meral Özsoyoglu: Statistical Database Query Languages. IEEE Trans. Software Eng. 11(10): 1071-1081(1985) BibTeX
Peter P. Chen: The Entity-Relationship Model - Toward a Unified View of Data. ACM Trans. Database Syst. 1(1): 9-36(1976) BibTeX

Referenced by

  1. Christos Faloutsos, H. V. Jagadish, Nikolaos Sidiropoulos: Recovering Information from Summary Data. VLDB 1997: 36-45
  2. Amihai Motro: Intensional Answers to Database Queries. IEEE Trans. Knowl. Data Eng. 6(3): 444-454(1994)
  3. Robert L. Read, Donald S. Fussell, Abraham Silberschatz: A Multi-Resolution Relational Data Model. VLDB 1992: 139-150
  4. Wesley W. Chu, Rei-Chi Lee, Qiming Chen: Using Type Inference and Induced Rules to Provide Intensional Answers. ICDE 1991: 396-403
  5. Amihai Motro, Qiuhui Yuan: Querying Database Knowledge. SIGMOD Conference 1990: 173-183
  6. Amihai Motro: Using Integrity Constraints to Provide Intensional Answers to Relational Queries. VLDB 1989: 237-246
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