| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432336 |
| 005 | | 20200224120941 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085747899 |
| 035 | |
▼a (MiAaPQ)AAI13896518 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Alarabi, Louai. |
| 245 | 10 |
▼a ST-Hadoop: A MapReduce Framework for Big Spatio-Temporal Data Management. |
| 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: 81-04, Section: B. |
| 500 | |
▼a Advisor: Mokbel, Mohamed F. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Apache Hadoop, employing the MapReduce programming paradigm, that has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not genuinely exploited towards processing large scale spatio-temporal data, especially with the emergence and popularity of applications that create them in large-scale. The huge volumes of spatio-temporal data come from applications, like Taxi fleet in urban computing, Asteroids in astronomy research studies, animal movements in habitat studies, neuron analysis in neuroscience research studies, and contents of social networks (e.g., Twitter or Facebook). Managing space and time are two fundamental characteristics that raised the demand for processing spatio-temporal data created by these applications. Besides the massive size of data, the complexity of shapes and formats associated with these data raised many challenges in managing spatio-temporal data.The goal of the dissertation is centered on establishing a full-fledged big spatio-temporal data management system that serves the need for a wide range of spatio-temporal applications. This involves indexing, querying, and analyzing spatio-temporal data. We propose ST-Hadoop |
| 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 81-04B. |
| 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=T15491722
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |