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  1. Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '13)
  2. EHSTC: an enhanced method for semantic trajectory compression
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Towards window stream queries over continuous phenomena
Spatiotemporal transformation of social media geostreams: a case study of Twitter for flu risk analysis
Enhanced database support for location-based services
A novel real-time framework for extracting patterns from trajectory data streams
Automatic identification of points of interest in global navigation satellite system data: a spatial temporal approach
EHSTC: an enhanced method for semantic trajectory compression
ADTOS: arrival departure tradeoff optimization system
Geo-clouds: visualizing news over geographical maps
Clustering spatial data streams for targeted alerting in disaster response
Continuum: a spatiotemporal data model to represent and qualify filiation relationships
Mining robust neighborhoods for quality control of sensor data

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EHSTC: an enhanced method for semantic trajectory compression

Content Provider ACM Digital Library
Author Feng, Shenzhu Zheng, Ning Xu, Ming Zhang, Xiaofei Xu, Jian
Abstract The increasing of location aware mobile devices such as vehicle navigation equipment and smart phones has enabled the collection of massive trajectories data. Movement trajectory compression has become an urgent necessity to store these data. Traditional algorithms for trajectory compression are based on the location distribution of sampling points, and often lead to intolerable error with a high compression rate. In urban road network, the movements of vehicles are usually bounded by road network. An initial thought of how to make use of semantics in trajectory compression is to represent the compressed trajectory in road segments with the entry time and the leaving time information attached. However, the movement of moving object during the road is completely abandoned. This paper has proposed an algorithm named enhanced semantic trajectory compression (EHSTC) that compress trajectories based on road semantics as well as motion feature. During chunking sampling points in a road segment, those points with great motion feature changes will be detected and stored in the feature point list of underlying road segment. The experimental result on real trajectories demonstrates the effectiveness and efficiency of the proposed solution.
Starting Page 43
Ending Page 49
Page Count 7
File Format PDF
ISBN 9781450325325
DOI 10.1145/2534303.2534306
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Motion feature Trajectory compression Road semantics
Content Type Text
Resource Type Article
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