User Driven Multi-Criteria Source SelectionCitation formats

  • External authors:
  • Edward Abel
  • John Keane
  • Alvaro Fernandes
  • Martin Koehler
  • Nikolaos Konstantinou
  • Nurzety Binti Ahmad Azuan

Standard

User Driven Multi-Criteria Source Selection. / Abel, Edward; Keane, John; Paton, Norman; Fernandes, Alvaro; Koehler, Martin; Konstantinou, Nikolaos; Cortés Ríos, Julio César; Binti Ahmad Azuan, Nurzety; Embury, Suzanne.

In: Information Sciences, Vol. 430-431, 15.11.2017, p. 179-199.

Research output: Contribution to journalArticlepeer-review

Harvard

Abel, E, Keane, J, Paton, N, Fernandes, A, Koehler, M, Konstantinou, N, Cortés Ríos, JC, Binti Ahmad Azuan, N & Embury, S 2017, 'User Driven Multi-Criteria Source Selection', Information Sciences, vol. 430-431, pp. 179-199. https://doi.org/10.1016/j.ins.2017.11.019

APA

Abel, E., Keane, J., Paton, N., Fernandes, A., Koehler, M., Konstantinou, N., Cortés Ríos, J. C., Binti Ahmad Azuan, N., & Embury, S. (2017). User Driven Multi-Criteria Source Selection. Information Sciences, 430-431, 179-199. https://doi.org/10.1016/j.ins.2017.11.019

Vancouver

Abel E, Keane J, Paton N, Fernandes A, Koehler M, Konstantinou N et al. User Driven Multi-Criteria Source Selection. Information Sciences. 2017 Nov 15;430-431:179-199. https://doi.org/10.1016/j.ins.2017.11.019

Author

Abel, Edward ; Keane, John ; Paton, Norman ; Fernandes, Alvaro ; Koehler, Martin ; Konstantinou, Nikolaos ; Cortés Ríos, Julio César ; Binti Ahmad Azuan, Nurzety ; Embury, Suzanne. / User Driven Multi-Criteria Source Selection. In: Information Sciences. 2017 ; Vol. 430-431. pp. 179-199.

Bibtex

@article{890ecfb122c04a79b38755915bdc2f73,
title = "User Driven Multi-Criteria Source Selection",
abstract = "Source selection is the problem of identifying a subset of available data sources that best meet a user{\textquoteright}s needs. In this paper we propose a user-driven approach to source selection that seeks to identify sources that are most fit for purpose. The approach employs a decision support methodology to take account of a user{\textquoteright}s context, to allow end users to tune their preferences by specifying the relative importance between different criteria, looking to find a trade-off solution aligned with his/her preferences. The approach is extensible to incorporate diverse criteria, not drawn from a fixed set, and solutions can use a subset of the data from each selected source, rather than require that sources are used in their entirety or not at all.The paper describes and motivates the approach, presenting a methodology for modelling a user{\textquoteright}s context, and its collection of optimisation algorithms for exploring the space of solutions, and compares and evaluates the resulting algorithms using multiple real world data sets. The experiments show how source selection results are produced that are attuned to each user{\textquoteright}s preferences, both with respect to overall weighted utility and through faithful representation of a user{\textquoteright}s preferences within a result, while scaling to potentially thousands of sources.",
keywords = "Data science, Data wrangling, Information retrieval, Multi-criteria decision analysis, Multi-objective optimization, Source selection",
author = "Edward Abel and John Keane and Norman Paton and Alvaro Fernandes and Martin Koehler and Nikolaos Konstantinou and {Cort{\'e}s R{\'i}os}, {Julio C{\'e}sar} and {Binti Ahmad Azuan}, Nurzety and Suzanne Embury",
year = "2017",
month = nov,
day = "15",
doi = "10.1016/j.ins.2017.11.019",
language = "English",
volume = "430-431",
pages = "179--199",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - 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 - Cortés Ríos, Julio César

AU - Binti Ahmad Azuan, Nurzety

AU - Embury, Suzanne

PY - 2017/11/15

Y1 - 2017/11/15

N2 - Source selection is the problem of identifying a subset of available data sources that best meet a user’s needs. In this paper we propose a user-driven approach to source selection that seeks to identify sources that are most fit for purpose. The approach employs a decision support methodology to take account of a user’s context, to allow end users to tune their preferences by specifying the relative importance between different criteria, looking to find a trade-off solution aligned with his/her preferences. The approach is extensible to incorporate diverse criteria, not drawn from a fixed set, and solutions can use a subset of the data from each selected source, rather than require that sources are used in their entirety or not at all.The paper describes and motivates the approach, presenting a methodology for modelling a user’s context, and its collection of optimisation algorithms for exploring the space of solutions, and compares and evaluates the resulting algorithms using multiple real world data sets. The experiments show how source selection results are produced that are attuned to each user’s preferences, both with respect to overall weighted utility and through faithful representation of a user’s preferences within a result, while scaling to potentially thousands of sources.

AB - Source selection is the problem of identifying a subset of available data sources that best meet a user’s needs. In this paper we propose a user-driven approach to source selection that seeks to identify sources that are most fit for purpose. The approach employs a decision support methodology to take account of a user’s context, to allow end users to tune their preferences by specifying the relative importance between different criteria, looking to find a trade-off solution aligned with his/her preferences. The approach is extensible to incorporate diverse criteria, not drawn from a fixed set, and solutions can use a subset of the data from each selected source, rather than require that sources are used in their entirety or not at all.The paper describes and motivates the approach, presenting a methodology for modelling a user’s context, and its collection of optimisation algorithms for exploring the space of solutions, and compares and evaluates the resulting algorithms using multiple real world data sets. The experiments show how source selection results are produced that are attuned to each user’s preferences, both with respect to overall weighted utility and through faithful representation of a user’s preferences within a result, while scaling to potentially thousands of sources.

KW - Data science

KW - Data wrangling

KW - Information retrieval

KW - Multi-criteria decision analysis

KW - Multi-objective optimization

KW - Source selection

U2 - 10.1016/j.ins.2017.11.019

DO - 10.1016/j.ins.2017.11.019

M3 - Article

VL - 430-431

SP - 179

EP - 199

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -