Temporal probabilistic object bases

Veronica Biazzo*, Rosalba Giugno, Thomas Lukasiewicz, V. S. Subrahmanian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.

Original languageEnglish (US)
Pages (from-to)921-939
Number of pages19
JournalIEEE Transactions on Knowledge and Data Engineering
Volume15
Issue number4
DOIs
StatePublished - Jul 2003
Externally publishedYes

Keywords

  • Probabilistic databases
  • Temporal data
  • Uncertainty management

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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