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dc.contributor.authorLiao, Zhung-Xunen_US
dc.contributor.authorLei, Po-Rueyen_US
dc.contributor.authorShen, Tsu-Jouen_US
dc.contributor.authorLi, Shou-Chungen_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.date.accessioned2014-12-08T15:30:46Z-
dc.date.available2014-12-08T15:30:46Z-
dc.date.issued2012en_US
dc.identifier.isbn978-0-7695-4925-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/21978-
dc.identifier.urihttp://dx.doi.org/10.1109/ICDMW.2012.11en_US
dc.description.abstractDue to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. AppNow consists of three components, the usage logger, the temporal profile constructor and the Apps predictor. First, the usage logger records every App start time. Then, the temporal profiles are built by applying Discrete Fourier Transform and exploring usage periods and specific times. Finally, the system calculates the usage probability at current time for each App and shows a list of Apps with highest probability. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.en_US
dc.language.isoen_USen_US
dc.subjectmobile applicationen_US
dc.subjecttemporal profileen_US
dc.subjectpredictionen_US
dc.subjectdata miningen_US
dc.titleMining Temporal Profiles of Mobile Applications for Usage Predictionen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/ICDMW.2012.11en_US
dc.identifier.journal12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)en_US
dc.citation.spage890en_US
dc.citation.epage893en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000320946500123-
Appears in Collections:Conferences Paper


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