Handling Uncertainty and Ignorance in Databases: A Rule to Combine Dependent Data

R.S. Choenni, H.E. Blok, Erik Leertouwer

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)

Abstract

In many applications, uncertainty and ignorance go hand in hand. Therefore, to deliver database support for effective decision making, an integrated view of uncertainty and ignorance should be taken. So far, most of the efforts attempted to capture uncertainty and ignorance with probability theory. In this paper, we discuss the weakness to capture ignorance with probability theory, and propose an approach inspired by the Dempster-Shafer theory to capture uncertainty and ignorance. Then, we present a rule to combine dependent data that are represented in different relations. Such a rule is required to perform joins in a consistent way. We illustrate that our rule is able to solve the so-called problem of information loss, which was considered as an open problem so far.
Original languageUndefined
Title of host publicationProceedings of the 11th International Conference on Database Systems for Advanced Applications (DASFAA 2006)
EditorsMong Li Lee, Kian-Lee Tan, Vilas Wuwongse
Place of PublicationBerlin, Germany
PublisherSpringer
Pages310-324
Number of pages15
ISBN (Print)978-3-540-33337-1
DOIs
Publication statusPublished - Apr 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume3882
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • METIS-238694
  • IR-63575
  • EWI-7538

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