Uncertain Groupings: Probabilistic combination of grouping data

B. Wanders, Maurice van Keulen, P.E. van der Vet

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91 Downloads (Pure)

Abstract

Probabilistic approaches for data integration have much potential. We view data integration as an iterative process where data understanding gradually increases as the data scientist continuously refines his view on how to deal with learned intricacies like data conflicts. This paper presents a probabilistic approach for integrating data on groupings. We focus on a bio-informatics use case concerning homology. A bio-informatician has a large number of homology data sources to choose from. To enable querying combined knowledge contained in these sources, they need to be integrated. We validate our approach by integrating three real-world biological databases on homology in three iterations.
Original languageUndefined
Title of host publicationProceedings of the 26th International Conference on Database and Expert Systems Applications, DEXA 2015
Place of PublicationSwitzerland
PublisherSpringer
Pages236-250
Number of pages15
ISBN (Print)978-3-319-22848-8
DOIs
Publication statusPublished - 1 Sep 2015
Event26th International Conference on Database and Expert Systems Applications, DEXA 2015 - Valencia, Spain
Duration: 1 Sep 20154 Sep 2015
Conference number: 26
http://www.dexa.org/previous/dexa2015/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9261
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Database and Expert Systems Applications, DEXA 2015
Abbreviated titleDEXA
CountrySpain
CityValencia
Period1/09/154/09/15
Internet address

Keywords

  • EWI-26266
  • groupings
  • METIS-314958
  • IR-98148
  • probabilistic database

Cite this

Wanders, B., van Keulen, M., & van der Vet, P. E. (2015). Uncertain Groupings: Probabilistic combination of grouping data. In Proceedings of the 26th International Conference on Database and Expert Systems Applications, DEXA 2015 (pp. 236-250). (Lecture Notes in Computer Science; Vol. 9261). Switzerland: Springer. https://doi.org/10.1007/978-3-319-22849-5_17