Towards best-effort merge of taxonomically organized data

David Thau, Shawn Bowers, Bertram Ludaescher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the task of merging datasets that have been organized using different, but aligned taxonomies. We assume such a merge is intended to create a single dataset that unambiguously describes the information in the source datasets using the alignment. We also assume that the merged result should reflect the observations of the datasets as specifically as possible. Typically, there will be no single merge result that is both unambiguous and maximally specific. In this case, a user may be provided with a set of possible merged datasets. If the user requires a single dataset, that dataset loses specificity. Here we examine whether the data exchange setting can provide a way to derive a "best-effort" merge. We find that the data exchange setting might be a good candidate for providing the merge, but further research is needed.

Original languageEnglish (US)
Title of host publicationICDE Workshops 2010 - The 2010 IEEE 26th International Conference on Data Engineering Workshops
Pages151-154
Number of pages4
DOIs
StatePublished - May 28 2010
Externally publishedYes
Event2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010 - Long Beach, CA, United States
Duration: Mar 1 2010Mar 6 2010

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010
CountryUnited States
CityLong Beach, CA
Period3/1/103/6/10

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Thau, D., Bowers, S., & Ludaescher, B. (2010). Towards best-effort merge of taxonomically organized data. In ICDE Workshops 2010 - The 2010 IEEE 26th International Conference on Data Engineering Workshops (pp. 151-154). [5452756] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDEW.2010.5452756