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dc.contributor.authorCong, Phanen_US
dc.contributor.authorNguyen, Thanhen_US
dc.contributor.authorNguyen, Quoc Viet Hungen_US
dc.contributor.authorStantic, Belaen_US
dc.contributor.editorRajendra Akerkar, Mirjana Ivanovic, Sang-Wook Kim, Yannis Manolopoulos, et al.
dc.date.accessioned2019-05-29T12:44:19Z
dc.date.available2019-05-29T12:44:19Z
dc.date.issued2018en_US
dc.identifier.isbn9781450354899en_US
dc.identifier.doi10.1145/3227609.3227678en_US
dc.identifier.urihttp://hdl.handle.net/10072/380382
dc.description.abstractParticipatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for world-wide applications, without spending cost to buy dedicated sensors. Despite of this benefit, the data collected from human sensors are inherently uncertain due to no quality guarantee from the participants. Moreover, the participatory sensing data are time series that not only exhibit highly irregular dependencies on time, but also vary from sensor to sensor. To overcome these issues, we study in this paper the problem of reconciling probabilistic data from given (uncertain) time series collected by participatory sensors. More precisely, an iterative process is executed in which we exchange between two mutual reinforcing routines: (i) aggregating probabilistic time series from multiple sensors and expert input, (ii) validating them by expert knowledge with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofconferencenameWIMS 2018en_US
dc.relation.ispartofconferencetitleProceedings of the 8th International Conference on Web Intelligence, Mining and Semanticsen_US
dc.relation.ispartofdatefrom2018-06-25en_US
dc.relation.ispartofdateto2018-06-27en_US
dc.relation.ispartoflocationNovi Sad, Serbiaen_US
dc.subject.fieldofresearchDatabase Managementen_US
dc.subject.fieldofresearchcode080604en_US
dc.titleMinimizing Efforts in Reconciling Participatory Sensing Dataen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conferencesen_US
dc.type.codeE - Conference Publicationsen_US
dc.description.versionPost-printen_US
gro.rights.copyright© ACM 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, ISBN: 978-1-4503-5489-9, DOI: https://doi.org/10.1145/3227609.3227678en_US
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