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Addressing Challenges with Big Data ...

Dhar, Samir K.

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Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System./
作者:	Dhar, Samir K.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	118 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
Contained By:	Dissertation Abstracts International78-09B(E).
標題:	Information technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10592322
ISBN:	9781369733037

Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System.
Dhar, Samir K.

Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 118 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--The University of Toledo, 2016.

The study presented here deals with commercial vessel tracking in the Great Lakes using the Automatic Identification System (AIS). Specific objectives within this study include development of methods for data acquisition, data reduction, storage and management, and reporting of vessel activity within the Great Lakes using AIS. These data show considerable promise in tracking commodity flows through the system as well as documenting traffic volumes at key locations requiring infrastructure investment (particularly dredging). Other applications include detecting vessel calls at specific terminals, locks and other navigation points of interest. This study will document the techniques developed to acquire, reduce, aggregate and store AIS data at The University of Toledo. Specific topics of the paper include: data reducing techniques to reduce data volumes, vessel path tracking, estimate speed on waterway network, detection of vessel calls made at a dock, and a data analysis and mining for errors within AIS data. The study also revealed the importance of AIS technology in maritime safety, but the data is coupled with errors and inaccuracy. These errors within the AIS data will have to be addressed and rectified in future to make the data accurate and useful. The data reduction algorithm shows a 98% reduction in AIS data making it more manageable. In future similar data reduction techniques can possibly be used with traffic GPS data collected for highways and railways.

ISBN: 9781369733037Subjects--Topical Terms:

532993
Information technology.

Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System.
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