Aggregate Evaluability in Statistical Databases.

@inproceedings{DBLP:conf/vldb/MalvestutoM89,
  author    = {Francesco M. Malvestuto and
               Marina Moscarini},
  editor    = {Peter M. G. Apers and
               Gio Wiederhold},
  title     = {Aggregate Evaluability in Statistical Databases},
  booktitle = {Proceedings of the Fifteenth International Conference on Very
               Large Data Bases, August 22-25, 1989, Amsterdam, The Netherlands},
  publisher = {Morgan Kaufmann},
  year      = {1989},
  isbn      = {1-55860-101-5},
  pages     = {279-286},
  ee        = {db/conf/vldb/MalvestutoM89.html},
  crossref  = {DBLP:conf/vldb/89},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Usually a statistical database contains many summary tables representing the distribution of the same statistical variable over the classes ofas many partitions of a certain universe of objects. Existing query systems allow only queries on single tables. Indeed, in most cases additional queries can be evaluated by combining the information contained in similar tables in a suitable way.

In order to improve the responsiveness of the database and allow an integrated use of the stored information, we propose to inform the database system of the relationship among the partitions adopted in the tables. Such a relationship, called intersection dependency, states which classes of the partitions have a non-empty intersection and can be represented by a uniform multipartite hypergraph, called intersection hypergraph.

On the grounds of the algebraic properties of the intel section hypergraph and under the assumption of data additivity, we shall provide a characterization of evaluable queries, which allows us to define polynomial-time procedures both for testing evaluability and for evaluating queries.

Copyright © 1989 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. Chang Li, Xiaoyang Sean Wang: Optimizing Statistical Queries by Exploiting Orthogonality and Interval Properties of Grouping Relations. SSDBM 1996: 118-127
2. Francesco M. Malvestuto: A Universal-Scheme Approach to Statistical Databases Containing Homogeneous Summary Tables. ACM Trans. Database Syst. 18(4): 678-708(1993)
3. Francesco M. Malvestuto, Marina Moscarini, Maurizio Rafanelli: Suppressing Marginal Cells to Protect Sensitive Information in a Two-Dimensional Statistical Table. PODS 1991: 252-258
4. Francesco M. Malvestuto, Marina Moscarini: Query Evaluability in Statistical Databases. IEEE Trans. Knowl. Data Eng. 2(4): 425-430(1990)