Konferenzpaper

Jahr der Veröffentlichung: 2001

Seiten: 205-252

Zeitschrift: Annals of Mathematics and Artificial Intelligence

Bandnummer: 33

Heftnummer: 2-4

ISSN: 1012-2443

DOI Link: https://doi.org/10.1023/A:1013121110704

Konferenz: 1st International Symposium on Foundations of Information and Knowledge Systems (FoIKS 2000)

Verlag: Springer

Abstract: 
We present a probabilistic data model for complex values. More precisely, we introduce probabilistic complex value relations, which combine the concept of probabilistic relations with the idea of complex values in a uniform framework. We elaborate a model-theoretic definition of probabilistic combination strategies, which has a rigorous foundation on probability theory. We then define an algebra for querying database instances, which comprises the operations of selection, projection, renaming, join, Cartesian product, union, intersection, and difference. We prove that our data model and algebra for probabilistic complex values generalizes the classical relational data model and algebra. Moreover, we show that under certain assumptions, all our algebraic operations are tractable. We finally show that most of the query equivalences of classical relational algebra carry over to our algebra on probabilistic complex value relations. Hence, query optimization techniques for classical relational algebra can easily be applied to optimize queries on probabilistic complex value relations.

Harvard-Zitierstil: Eiter, T., Lukasiewicz, T. and Walter, M. (2001) A data model and algebra for probabilistic complex values, Annals of Mathematics and Artificial Intelligence, 33(2-4), pp. 205-252. https://doi.org/10.1023/A:1013121110704

APA-Zitierstil: Eiter, T., Lukasiewicz, T., & Walter, M. (2001). A data model and algebra for probabilistic complex values. Annals of Mathematics and Artificial Intelligence. 33(2-4), 205-252. https://doi.org/10.1023/A:1013121110704