Efficient Feedback Collection for Pay-as-you-go Source Selection
Cortés Ríos, Julio César; Paton, Norman W.; Fernandes, Alvaro A. A.; Belhajjame, Khalid (2016), Efficient Feedback Collection for Pay-as-you-go Source Selection, in Baumann, Peter; Manolescu-Goujot, Ioana; Trani, Luca, Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM '16), ACM Press : New York. 10.1145/2949689.2949690
Type
Communication / ConférenceDate
2016Conference title
28th International Conference on Scientific and Statistical Database Management (SSDBM '16)Conference date
2016-07Conference city
BudapestConference country
HungaryBook title
Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM '16)Book author
Baumann, Peter; Manolescu-Goujot, Ioana; Trani, LucaPublisher
ACM Press
Published in
New York
ISBN
978-1-4503-4215-5
Publication identifier
Metadata
Show full item recordAuthor(s)
Cortés Ríos, Julio CésarSchool of Computer Science [Manchester]
Paton, Norman W.
School of Computer Science [Manchester]
Fernandes, Alvaro A. A.
School of Computer Science [Manchester]
Belhajjame, Khalid
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
Technical developments, such as the web of data and web data extraction, combined with policy developments such as those relating to open government or open science, are leading to the availability of increasing numbers of data sources. Indeed, given these physical sources, it is then also possible to create further virtual sources that integrate, aggregate or summarise the data from the original sources. As a result, there is a plethora of data sources, from which a small subset may be able to provide the information required to support a task. The number and rate of change in the available sources is likely to make manual source selection and curation by experts impractical for many applications, leading to the need to pursue a pay-as-you-go approach, in which crowds or data consumers annotate results based on their correctness or suitability, with the resulting annotations used to inform, e.g., source selection algorithms. However, for pay-as-you-go feedback collection to be cost-effective, it may be necessary to select judiciously the data items on which feedback is to be obtained. This paper describes OLBP (Ordering and Labelling By Precision), a heuristics-based approach to the targeting of data items for feedback to support mapping and source selection tasks, where users express their preferences in terms of the trade-off between precision and recall. The proposed approach is then evaluated on two different scenarios, mapping selection with synthetic data, and source selection with real data produced by web data extraction. The results demonstrate a significant reduction in the amount of feedback required to reach user-provided objectives when using OLBP.Subjects / Keywords
Pay-as-you-go data integration; user feedbackRelated items
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