Towards automated prediction of relationships among scientific datasets

Abdussalam Alawini, David Maier, Kristin Tufte, Bill Howe, Rashmi Nandikur

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

Abstract

Before scientists can analyze, publish, or share their data, they often need to determine how their datasets are re-lated. Determining relationships helps scientists identify the most complete version of a dataset, detect versions of datasets that complement each other, and determine multi-ple datasets that overlap. In previous work, we showed how observable relationships between two datasets help scientists recall their original derivation connection. While that work helped with identifying relationships between two datasets, it is infeasible for scientists to use it for finding relationships between all possible pairs in a large collection of datasets. In order to deal with larger numbers of datasets, we are ex-Tending our methodology with a relationship-prediction sys-Tem, ReDiscover, a tool to identify pairs from a collection of datasets that are most likely related and the relationship between them. We report on the initial design of ReDis-cover, which uses machine-learning methods such as Condi-Tional Random Fields and Support Vector Machines to the relationship-discovery problem. Our preliminarily evalua-Tion shows that ReDiscover predicted relationships with an average accuracy of 87%.

Original languageEnglish (US)
Title of host publicationSSDBM 2015 - Proceedings of the 27th International Conference on Scientific and Statistical Database Management
EditorsAmarnath Gupta, Susan Rathbun
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450337090
DOIs
StatePublished - Jun 29 2015
Externally publishedYes
Event27th International Conference on Scientific and Statistical Database Management, SSDBM 2015 - San Diego, United States
Duration: Jun 29 2015Jul 1 2015

Publication series

NameACM International Conference Proceeding Series
Volume29-June-2015

Other

Other27th International Conference on Scientific and Statistical Database Management, SSDBM 2015
CountryUnited States
CitySan Diego
Period6/29/157/1/15

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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