Analytics over probabilistic unmerged duplicates

Ekaterini Ioannou, Minos N. Garofalakis

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

Abstract

This paper introduces probabilistic databases with unmerged duplicates (DB ud ), i.e., databases containing probabilistic information about instances found to describe the same real-world objects. We discuss the need for efficiently querying such databases and for supporting practical query scenarios that require analytical or summarized information. We also sketch possible methodologies and techniques that would allow performing efficient processing of queries over such probabilistic databases, and especially without the need to materialize the (potentially, huge) collection of all possible deduplication worlds.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Scalable Uncertainty Management (SUM2014)
Place of PublicationCham
PublisherSpringer
Pages203-208
ISBN (Print)9783319115078
DOIs
Publication statusPublished - 2014
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
Volume8720

Cite this

Ioannou, E., & Garofalakis, M. N. (2014). Analytics over probabilistic unmerged duplicates. In Proceedings of the International Conference on Scalable Uncertainty Management (SUM2014) (pp. 203-208). (Lecture Notes in Computer Science; Vol. 8720). Springer. https://doi.org/10.1007/978-3-319-11508-5_17