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dc.contributor.authorNguyen, Quoe Viet Hung
dc.contributor.authorSathe, Saket
dc.contributor.authorDuong, Chi Thang
dc.contributor.authorAberer, Karl
dc.date.accessioned2021-07-15T04:18:52Z
dc.date.available2021-07-15T04:18:52Z
dc.date.issued2014
dc.identifier.isbn9781631900433en_US
dc.identifier.doi10.4108/icst.collaboratecom.2014.257239en_US
dc.identifier.urihttp://hdl.handle.net/10072/405995
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 creating probabilistic data from given (uncertain) time series collected by participatory sensors. We approach the problem in two steps. In the first step, we generate probabilistic times series from raw time series using a dynamical model from the time series literature. In the second step, we combine probabilistic time series from multiple sensors based on the mutual relationship between the reliability of the sensors and the quality of their data. Through extensive experimentation, we demonstrate the efficiency of our approach on both real data and synthetic data.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherIEEEen_US
dc.relation.ispartofconferencename10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharingen_US
dc.relation.ispartofconferencetitle10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharingen_US
dc.relation.ispartofdatefrom2014-10-22
dc.relation.ispartofdateto2014-10-25
dc.relation.ispartoflocationMiami, FL, USAen_US
dc.relation.ispartofpagefrom114en_US
dc.relation.ispartofpageto123en_US
dc.subject.fieldofresearchNetworking and Communicationsen_US
dc.subject.fieldofresearchcode080503en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsEngineering, Electrical & Electronicen_US
dc.subject.keywordsparticipatory sensingen_US
dc.titleTowards Enabling Probabilistic Databases for Participatory Sensingen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conferencesen_US
dcterms.bibliographicCitationNguyen, QVH; Sathe, S; Duong, CT; Aberer, K, Towards Enabling Probabilistic Databases for Participatory Sensing, 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2014, pp. 114-123en_US
dc.date.updated2021-07-15T04:15:57Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© 2014IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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