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An Adaptive Framework for Real-Time ...

Sharker, Md Monir Hossain.

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An Adaptive Framework for Real-Time Spatiotemporal Big Data Analytics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Adaptive Framework for Real-Time Spatiotemporal Big Data Analytics./
作者:	Sharker, Md Monir Hossain.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	159 p.
附註:	Source: Dissertations Abstracts International, Volume: 79-11, Section: A.
Contained By:	Dissertations Abstracts International79-11A.
標題:	Geographic information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10831884
ISBN:	9780355887211

An Adaptive Framework for Real-Time Spatiotemporal Big Data Analytics.
Sharker, Md Monir Hossain.

An Adaptive Framework for Real-Time Spatiotemporal Big Data Analytics. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 159 p.

Source: Dissertations Abstracts International, Volume: 79-11, Section: A.

Thesis (Ph.D.)--University of Pittsburgh, 2017.

This item must not be sold to any third party vendors.

Due to advancements in and widespread usage of technologies such as smartphones, satellites, smart sensors, and social networks, collection of spatiotemporal data is growing rapidly. Such massive spatiotemporal data require appropriate techniques and technologies for their efficient analysis and processing. Analyzing massive spatiotemporal data efficiently and effectively is challenging since the data changes dynamically over space and time whereas, often, decisions followed by the analysis need to be made under real-time constraints. Compared to non-spatial data, spatiotemporal data, among other unique characteristics, are multidimensional (x, y, attributes, time) in nature, complex in structures and behaviors, and provides details at different resolutions and scales. These characteristics together make analyzing and processing massive spatiotemporal data in real time a challenging task. Resorting to high-performance computing (HPC) is a common approach for handling this computing challenge but to determine optimal solutions through data and computation analysis, appropriate analytics and computing solutions are needed. In this dissertation, we proposed a framework which is basically a platform providing spatiotemporal data-intensive analytics for data- and compute-intensive applications that require computation under real-time constraints on given computing resources. The framework is a layered structure consisting of four interrelated components (layers); three on analytics and one on adaptive computing. A graph-based approach is developed as the foundation of the analytics components which are: efficient analytics - providing acceptable solutions based on current data in the absence of historical data; predictive analytics - providing near-optimal solutions by learning from the patterns of historical data and predicting based on the learning; meta-analytics - providing optimal solutions by analyzing pattern of past data patterns; and adaptive computing that ensures appropriate analytics are applied and computation is completed in real time on available computing resources.

ISBN: 9780355887211Subjects--Topical Terms:

3432445
Geographic information science.

An Adaptive Framework for Real-Time Spatiotemporal Big Data Analytics.
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