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A Novel Compression Algorithm for Spatiotemporal Data Based on PSO and GA
Wu JW(吴俊伟); Zhu YL(朱云龙); Ku T(库涛); Wang L(王亮)
作者部门信息服务与智能控制技术研究室
会议名称International Conference on Computational Intelligence and Software Engineering (CiSE 2012)
会议日期December 14-16, 2012
会议地点Wuhan, China
会议录名称International Conference on Computational Intelligence and Software Engineering (CiSE 2012)
出版者Scientific Research Publishing
出版地USA
2012
页码13-16
产权排序1
ISBN号978-1-61896-036-8
关键词Spatiotemporal Data Compression Genetic Algorithm Particle Swarm Optimization Douglas-peucker Algorithm
摘要Positioning applications have recently become extremely popular thanks to recent advances in telecommunications and geopositioning reporting devices (GPS, PDA etc.). It is thus to be expected that all these devices will start to generate an unprecedented stream of time-stamped positions leading to storage and computation challenges. Hence the need for trajectory compression arises. Previously most work has been done in compression mainly deal with two-dimensional spatial data without taking into account another important dimension: time. Besides, very few of the existing spatiotemporal compression algorithms, which cannot determine whether the final compression result is optimal, use optimization algorithms such as Genetic Algorithm (GA) and Particle Swam Optimation (PSO) algorithm. With these two points in mind, we propose a new trajectory compression algorithm based on the GA, PSO, and Douglas-Peucker algorithms to compress time-stamped position data. Experimental results illustrate that this compression method can archive large data compression ratio and high accuracy, and especially, can obtain the optimal or suboptimum compression result.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/13824
专题信息服务与智能控制技术研究室
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Wu JW,Zhu YL,Ku T,et al. A Novel Compression Algorithm for Spatiotemporal Data Based on PSO and GA[C]. USA:Scientific Research Publishing,2012:13-16.
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