| LDR | | 01770nam u200361 4500 |
| 001 | | 000000419550 |
| 005 | | 20190215163751 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438136137 |
| 035 | |
▼a (MiAaPQ)AAI10903788 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Wu, Fei. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a The Pennsylvania State University.
▼b Information Sciences and Technology. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000745
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |