Agatha: Predicting Daily Activities from Place Visit History for Activity-Aware Mobile Services in Smart Cities

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dc.contributor.authorKim, Byoungjipko
dc.contributor.authorKang, Seungwooko
dc.contributor.authorHa, Jin-Youngko
dc.contributor.authorSong, Junehwako
dc.date.accessioned2016-04-20T07:03:15Z-
dc.date.available2016-04-20T07:03:15Z-
dc.date.created2016-01-04-
dc.date.created2016-01-04-
dc.date.created2016-01-04-
dc.date.issued2015-
dc.identifier.citationINTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS-
dc.identifier.issn1550-1329-
dc.identifier.urihttp://hdl.handle.net/10203/205660-
dc.description.abstractWe present a place-history-based activity prediction system called Agatha, in order to enable activity-aware mobile services in smart cities. The system predicts a user's potential subsequent activities that are highly likely to occur given a series of information about activities done before or activity-related contextual information such as visit place and time. To predict the activities, we develop a causality-based activity prediction model using Bayesian networks. The basic idea of the prediction is that where a person has been and what he/she has done so far influence what he/she will do next. To show the feasibility, we evaluate the prediction model using the American Time-Use Survey (ATUS) dataset, which includes more than 10,000 people's location and activity history. Our evaluation shows that Agatha can predict users' potential activities with up to 90% accuracy for the top 3 activities, more than 80% for the top 2 activities, and about 65% for the top 1 activity while considering a relatively large number of daily activities defined in the ATUS dataset, that is, 17 activities.-
dc.languageEnglish-
dc.publisherHINDAWI PUBLISHING CORP-
dc.titleAgatha: Predicting Daily Activities from Place Visit History for Activity-Aware Mobile Services in Smart Cities-
dc.typeArticle-
dc.identifier.wosid000366424900001-
dc.identifier.scopusid2-s2.0-84950132659-
dc.type.rimsART-
dc.citation.publicationnameINTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS-
dc.identifier.doi10.1155/2015/867602-
dc.contributor.localauthorSong, Junehwa-
dc.contributor.nonIdAuthorKim, Byoungjip-
dc.contributor.nonIdAuthorKang, Seungwoo-
dc.contributor.nonIdAuthorHa, Jin-Young-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
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