| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435394 |
| 005 | | 20200228094543 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085601955 |
| 035 | |
▼a (MiAaPQ)AAI13884603 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Tang, Xun. |
| 245 | 10 |
▼a Data Science for Mining Patterns in Spatial Events. |
| 260 | |
▼a [S.l.]:
▼b University of Minnesota.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 136 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-02, Section: B. |
| 500 | |
▼a Advisor: Shekhar, Shashi. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a There has been an explosive growth of spatial data over the last decades thanks to the popularity of location-based services (e.g., Google Maps), affordable devices (e.g., mobile phone with GPS receiver), and fast development of data transfer and storage technologies. This significant growth as well as the emergence of new technologies emphasize the need for automated discovery of spatial patterns which can facilitate applications such as mechanical engineering, transportation engineering, and public safety.This thesis investigates novel and societally important patterns from various types of large scale spatial events such as spatial point events, spatio-temporal point events, and spatio-temporal linear events. Multiple novel approaches are proposed to address the statistical, computational, and mathematical challenges posed by different patterns. Specifically, a neighbor node filter and a shortest tree pruning algorithms are developed to discover linear hotspots on shortest paths, a bi-directional fragment-multi- graph traversal is proposed for discovering linear hotspots on all simple paths, and an apriori-graph traversal algorithm is proposed to detecting spatio-temporal intersection patterns.Extensive theoretical and experimental analyses show that the proposed approaches not only achieve substantial computational efficiency but also guarantee mathematical properties such as solution correctness and completeness. Case studies using real-world datasets demonstrate that the proposed approaches identify valuable patterns that are not detected by current state-of-the-art methods. |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Minnesota.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-02B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491379
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |