Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2017.11.019 | Information Sciences |
Keywords | Field | DocType |
Source selection,Multi-criteria decision analysis,Multi-objective optimization,Information retrieval,Data science,Data wrangling | Faithful representation,Data set,End user,Decision support system,Artificial intelligence,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
430 | 0020-0255 | 5 |
PageRank | References | Authors |
0.46 | 20 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edward Abel | 1 | 24 | 4.85 |
| John A. Keane | 2 | 695 | 92.81 |
| Norman W. Paton | 3 | 3059 | 359.26 |
| Alvaro A. A. Fernandes | 4 | 14 | 3.65 |
| Martin Koehler | 5 | 56 | 8.05 |
| Nikolaos Konstantinou | 6 | 88 | 10.73 |
| J. C. Cortes-Rios | 7 | 34 | 3.35 |
| Nurzety A. Azuan | 8 | 6 | 1.51 |
| Suzanne M. Embury | 9 | 11 | 1.92 |