SOURCERY: User Driven Multi-Criteria Source Selection

Edward Abel, John Keane, Norman Paton, Alvaro Fernandes, Martin Koehler, Nikolaos Konstantinou, Nurzety A. Azuan, Suzanne Embury

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

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Abstract

Data scientists are usually interested in a subset of sources with
properties that are most aligned to intended data use. The SOURCERY
system supports interactive multi-criteria user-driven source selection.
SOURCERY allows a user to identify criteria they consider
of importance and indicate their relative importance, and seeks a
source selection result aligned to the user-supplied criteria preferences.
The user is given an overview of the properties of the
sources that are selected along with visual analyses contextualizing
the result in relation to what is theoretically possible and what is
possible given the set of available sources. The system also enables
a user to interactively perform iterative fine-tuning to explore how
changes to preferences may impact results.
Original languageEnglish
Title of host publicationProceedings of the 27th ACM International Conference on Information and Knowledge Management
Pages1947-1950
Number of pages3
DOIs
Publication statusPublished - 2018
EventThe 27th ACM International Conference on Information and Knowledge
Management
- Torino, Italy
Duration: 22 Oct 201826 Oct 2018

Conference

ConferenceThe 27th ACM International Conference on Information and Knowledge
Management
Abbreviated titleACM CIKM '18
Country/TerritoryItaly
CityTorino
Period22/10/1826/10/18

Research Beacons, Institutes and Platforms

  • Manchester Institute of Biotechnology

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