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020 ▼a 9780438136137
035 ▼a (MiAaPQ)AAI10903788
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 004
1001 ▼a Wu, Fei.
24510 ▼a Mining Heterogeneous Data for Semantic Understanding of Mobility Data.
260 ▼a [S.l.] : ▼b The Pennsylvania State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 121 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
5021 ▼a Thesis (Ph.D.)--The Pennsylvania State University, 2018.
520 ▼a With the prevalence of positioning technology, an increasing amount of human mobility data becomes available nowadays, including geotagged social media data, location records collected by mobile phone applications, and GPS traces collected by na
520 ▼a This dissertation describes several recent attempts in fusing external context data for understanding the human mobility data. I will motivate the problem by presenting one key limitation of conventional mobility pattern mining approaches. A fun
590 ▼a School code: 0176.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a The Pennsylvania State University. ▼b Information Sciences and Technology.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0176
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000745 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자