Name
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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3269206.3269209 | CIKM |
Keywords | Field | DocType |
Source selection, Multi-criteria decision analysis, optimization | Data mining,Multiple-criteria decision analysis,Information retrieval,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-6014-2 | 0 | 0.34 |
References | Authors | |
7 | 8 | |
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 |
| Nurzety A. Azuan | 7 | 6 | 1.51 |
| Suzanne M. Embury | 8 | 11 | 1.92 |