ACM SIGMOD Anthology VLDB dblp.uni-trier.de

The Active Information System: A Data-Driven System for the Analysis of Imprecise Data.

Christine A. Montgomery, Enrique H. Ruspini: The Active Information System: A Data-Driven System for the Analysis of Imprecise Data. VLDB 1981: 376-384
@inproceedings{DBLP:conf/vldb/MontgomeryR81,
  author    = {Christine A. Montgomery and
               Enrique H. Ruspini},
  title     = {The Active Information System: A Data-Driven System for the Analysis
               of Imprecise Data},
  booktitle = {Very Large Data Bases, 7th International Conference, September
               9-11, 1981, Cannes, France, Proceedings},
  publisher = {IEEE Computer Society},
  year      = {1981},
  pages     = {376-384},
  ee        = {db/conf/vldb/MontgomeryR81.html},
  crossref  = {DBLP:conf/vldb/81},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

The concept of Active information System is introduced. Active Information Systems are collections of autonomous, data-driven tools operating in a data base environment and seeking to produce results that enhance the understanda- bility of the contents of the data base. The activities of the corresponding multiple concurrent processes are coordi- nated by controi mechanisms that perform in a goal oriented manner. The data models supported by Active Systems are characterized by the existence of numerous data elements that are known only in imprecise or uncertain terms.

The different types of components of Active Systems are identified and their organization into a prototypical architec- ture is described. Emphasis is placed on algorithms that assist users in hypothesis generation and verification tasks. These algorithms are based on concepts from data base technology, knowledge based systems, system science and multivalued logic.

The technological approaches utilized in the development of an Active System testbed to treat problems of natural language understanding and uncertainty representation are discussed in detail.

Copyright © 1981 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.


ACM SIGMOD Anthology

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Printed Edition

Very Large Data Bases, 7th International Conference, September 9-11, 1981, Cannes, France, Proceedings. IEEE Computer Society 1981
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References

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Enrique H. Ruspini: A New Approach to Clustering. Information and Control 15(1): 22-32(1969) BibTeX
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Steven A. Vere: Multilevel Counterfactuals for Generalizations of Relational Concepts and Productions. Artif. Intell. 14(2): 139-164(1980) BibTeX
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Referenced by

  1. Erol Gelenbe, Georges Hébrail: A Probability Model of Uncertainty in Data Bases. ICDE 1986: 328-333
  2. José Mauro Volkmer de Castilho, Marco A. Casanova, Antonio L. Furtado: A Temporal Framework for Database Specifications. VLDB 1982: 280-291
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