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Data Science for Mining Patterns in Spatial Events
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Data Science for Mining Patterns in Spatial Events. |
| 개인저자 | Tang, Xun. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 136 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085601955 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Shekhar, Shashi. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be sold to any third party vendors. |
| 요약 | 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. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
