SOURCERY: User Driven Multi-Criteria Source Selection

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

doi:10.1145/3269206.3269209

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/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Pages	1947-1950
Number of pages	3
DOIs	https://doi.org/10.1145/3269206.3269209
Publication status	Published - 2018
Event	The 27th ACM International Conference on Information and Knowledge Management - Torino, Italy Duration: 22 Oct 2018 → 26 Oct 2018

Conference

Conference	The 27th ACM International Conference on Information and Knowledge Management
Abbreviated title	ACM CIKM '18
Country/Territory	Italy
City	Torino
Period	22/10/18 → 26/10/18

Research Beacons, Institutes and Platforms

Manchester Institute of Biotechnology

Access to Document

10.1145/3269206.3269209

cikm-c-readyAccepted author manuscript, 511 KB

Cite this

@inproceedings{7e0d78110b5b40ee977543f2327361a2,

title = "SOURCERY: User Driven Multi-Criteria Source Selection",

abstract = "Data scientists are usually interested in a subset of sources withproperties that are most aligned to intended data use. The SOURCERYsystem supports interactive multi-criteria user-driven source selection.SOURCERY allows a user to identify criteria they considerof importance and indicate their relative importance, and seeks asource selection result aligned to the user-supplied criteria preferences.The user is given an overview of the properties of thesources that are selected along with visual analyses contextualizingthe result in relation to what is theoretically possible and what ispossible given the set of available sources. The system also enablesa user to interactively perform iterative fine-tuning to explore howchanges to preferences may impact results.",

author = "Edward Abel and John Keane and Norman Paton and Alvaro Fernandes and Martin Koehler and Nikolaos Konstantinou and Azuan, {Nurzety A.} and Suzanne Embury",

year = "2018",

doi = "10.1145/3269206.3269209",

language = "English",

pages = "1947--1950",

booktitle = "Proceedings of the 27th ACM International Conference on Information and Knowledge Management",

note = "The 27th ACM International Conference on Information and Knowledge<br/>Management , ACM CIKM '18 ; Conference date: 22-10-2018 Through 26-10-2018",

}

Abel, E, Keane, J , Paton, N, Fernandes, A, Koehler, M, Konstantinou, N, Azuan, NA & Embury, S 2018, SOURCERY: User Driven Multi-Criteria Source Selection. in Proceedings of the 27th ACM International Conference on Information and Knowledge Management. pp. 1947-1950, The 27th ACM International Conference on Information and Knowledge
Management , Torino, Italy, 22/10/18. https://doi.org/10.1145/3269206.3269209

TY - GEN

T1 - SOURCERY: User Driven Multi-Criteria Source Selection

AU - Abel, Edward

AU - Keane, John

AU - Paton, Norman

AU - Fernandes, Alvaro

AU - Koehler, Martin

AU - Konstantinou, Nikolaos

AU - Azuan, Nurzety A.

AU - Embury, Suzanne

PY - 2018

Y1 - 2018

N2 - Data scientists are usually interested in a subset of sources withproperties that are most aligned to intended data use. The SOURCERYsystem supports interactive multi-criteria user-driven source selection.SOURCERY allows a user to identify criteria they considerof importance and indicate their relative importance, and seeks asource selection result aligned to the user-supplied criteria preferences.The user is given an overview of the properties of thesources that are selected along with visual analyses contextualizingthe result in relation to what is theoretically possible and what ispossible given the set of available sources. The system also enablesa user to interactively perform iterative fine-tuning to explore howchanges to preferences may impact results.

AB - Data scientists are usually interested in a subset of sources withproperties that are most aligned to intended data use. The SOURCERYsystem supports interactive multi-criteria user-driven source selection.SOURCERY allows a user to identify criteria they considerof importance and indicate their relative importance, and seeks asource selection result aligned to the user-supplied criteria preferences.The user is given an overview of the properties of thesources that are selected along with visual analyses contextualizingthe result in relation to what is theoretically possible and what ispossible given the set of available sources. The system also enablesa user to interactively perform iterative fine-tuning to explore howchanges to preferences may impact results.

U2 - 10.1145/3269206.3269209

DO - 10.1145/3269206.3269209

M3 - Conference contribution

SP - 1947

EP - 1950

BT - Proceedings of the 27th ACM International Conference on Information and Knowledge Management

T2 - The 27th ACM International Conference on Information and Knowledge<br/>Management

Y2 - 22 October 2018 through 26 October 2018

ER -

SOURCERY: User Driven Multi-Criteria Source Selection

Abstract

Conference

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