東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Compression of GPS trajectory data: ...

Muckell, Jonathan.

FindBook

Google Book

Amazon

博客來

Compression of GPS trajectory data: Benchmarking framework and new approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Compression of GPS trajectory data: Benchmarking framework and new approach./
作者:	Muckell, Jonathan.
面頁冊數:	71 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
Contained By:	Dissertation Abstracts International74-11B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3588148
ISBN:	9781303260810

Compression of GPS trajectory data: Benchmarking framework and new approach.
Muckell, Jonathan.

Compression of GPS trajectory data: Benchmarking framework and new approach. - 71 p.

Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.

Thesis (Ph.D.)--State University of New York at Albany, 2013.

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. This dissertation provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this dissertation presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. A key requirement for these algorithms is to minimize the loss of information essential to location-based applications. To address this requirement, this research introduces a new compression method called SQUISH (Spatial QUalIty Simplification Heuristic) that provides improved run-time performance and usability. A comprehensive comparison of SQUISH with other algorithms is carried out using the introduced benchmarking framework across three types of real-world datasets and three synthetic data generators.

ISBN: 9781303260810Subjects--Topical Terms:

523869
Computer science.

Compression of GPS trajectory data: Benchmarking framework and new approach.
LDR:02503nmm a2200277 4500 001 2069914
005 20160524150724.5
008 170521s2013 ||||||||||||||||| ||eng d
020 $a 9781303260810
035 $a (MiAaPQ)AAI3588148
035 $a AAI3588148
040 $a MiAaPQ $c MiAaPQ
100 1 $a Muckell, Jonathan. $3 3184923
245 1 0 $a Compression of GPS trajectory data: Benchmarking framework and new approach.
300 $a 71 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-11(E), Section: B.
500 $a Adviser: Catherine T. Lawson.
502 $a Thesis (Ph.D.)--State University of New York at Albany, 2013.
520 $a GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. This dissertation provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this dissertation presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. A key requirement for these algorithms is to minimize the loss of information essential to location-based applications. To address this requirement, this research introduces a new compression method called SQUISH (Spatial QUalIty Simplification Heuristic) that provides improved run-time performance and usability. A comprehensive comparison of SQUISH with other algorithms is carried out using the introduced benchmarking framework across three types of real-world datasets and three synthetic data generators.
590 $a School code: 0668.
650 4 $a Computer science. $3 523869
650 4 $a Geographic information science and geodesy. $3 2122917
690 $a 0984
690 $a 0370
710 2 $a State University of New York at Albany. $b Informatics. $3 3184924
773 0 $t Dissertation Abstracts International $g 74-11B(E).
790 $a 0668
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3588148