期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2013
卷号：2
期号：1
页码：132
出版社：International Journal of Computer and Information Technology
摘要：Trajectory analysis is one of the actively researched areas of spatio-temporal databases. Exploring and analysing large datasets of movement data has become a vital part of research in many disciplines and decision-making fields. The major challenge in analysing process of trajectory data is to visualize, understand and extract meaningful patterns out of millions of locations collected from Automatic Identification Systems (AIS) points. AIS are used in maritime environments to assist in tracking and monitoring vessel movements. AIS datasets are real movement datasets recorded from dynamic vessels and consisting of voluminous raw data. To analyse such datasets required a systematic and methodical process. The first phase focused on development of a decoder to extract significant information from raw data. The extracted information was then utilized to perform knowledge discovery on movement data from dynamic objects. A spatio-temporal approach was applied to perform trajectory analysis on decoded datasets. The paper focuses on optimization of information to discover hidden knowledge from raw datasets. The purpose of optimizing information is to conduct trajectory analysis in order to identify the characteristics of vessels in New Zealand waterways. The discovered knowledge can also be applied in other fields such as safety, security and additional navigational aids. The study used real movement datasets of maritime domain provided by Kordia New Zealand recorded between March 2011 and May 2011. The experimental results indicate that the proposed methods could be successfully applied to perform trajectory analysis of vessel movement
关键词：component; ; AIS; Trajectory; spatio-temporal; ; movement data
