An Overview of Moving Object Trajectory Compression Algorithms

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential inc...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2016; no. 2016; pp. 1 - 13
Main Authors: Li, Daxing, Yuan, Guan, Xia, Shixiong, Sun, Penghui
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
John Wiley & Sons, Inc
Subjects:
Algorithms
Compressing
Data analysis
Data compression
Data mining
Hot spots
Mathematical analysis
Mathematical problems
Object motion
Reserves
Semantics
Strategy
Trajectories
Trajectory analysis
China
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2016/6587309