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dc.contributor.authorCortés Ríos, Julio César
dc.contributor.authorPaton, Norman
dc.contributor.authorFernandes, Alvaro A. A.
dc.contributor.authorBelhajjame, Khalid
dc.date.accessioned2017-02-06T14:35:44Z
dc.date.available2017-02-06T14:35:44Z
dc.date.issued2016
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16230
dc.descriptionArticle No. 1en
dc.language.isoenen
dc.subjectPay-as-you-go data integrationen
dc.subjectuser feedbacken
dc.subject.ddc004en
dc.titleEfficient Feedback Collection for Pay-as-you-go Source Selectionen
dc.typeCommunication / Conférence
dc.description.abstractenTechnical 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.en
dc.relation.ispartoftitleProceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM '16)en
dc.relation.ispartofeditorBaumann, Peter
dc.relation.ispartofeditorManolescu-Goujot, Ioana
dc.relation.ispartofeditorTrani, Luca
dc.relation.ispartofpublnameACM Pressen
dc.relation.ispartofpublcityNew Yorken
dc.relation.ispartofdate2016-07
dc.subject.ddclabelInformatique généraleen
dc.relation.ispartofisbn978-1-4503-4215-5en
dc.relation.conftitle28th International Conference on Scientific and Statistical Database Management (SSDBM '16)en
dc.relation.confdate2016-07
dc.relation.confcityBudapesten
dc.relation.confcountryHungaryen
dc.relation.forthcomingnonen
dc.identifier.doi10.1145/2949689.2949690en
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2017-02-06T14:21:10Z
hal.person.labIds90659
hal.person.labIds90659
hal.person.labIds90659
hal.person.labIds989
hal.identifierhal-01457783*


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