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ST-Hadoop: A MapReduce Framework for Big Spatio-Temporal Data Management

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서명/저자사항ST-Hadoop: A MapReduce Framework for Big Spatio-Temporal Data Management.
개인저자Alarabi, Louai.
단체저자명University of Minnesota. Computer Science.
발행사항[S.l.]: University of Minnesota., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항136 p.
기본자료 저록Dissertations Abstracts International 81-04B.
Dissertation Abstract International
ISBN9781085747899
학위논문주기Thesis (Ph.D.)--University of Minnesota, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Mokbel, Mohamed F.
이용제한사항This item must not be sold to any third party vendors.
요약Apache 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
일반주제명Computer science.
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