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  1. Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '13)
  2. A novel real-time framework for extracting patterns from trajectory data streams
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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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A novel real-time framework for extracting patterns from trajectory data streams

Content Provider ACM Digital Library
Author Boulanger, Mathilda Yang, Hanqing Gruenwald, Le
Abstract The rapid development and deployment of location-acquisition equipment such as GPS systems and GSM communication networks has made collection of spatio-temporal trajectory datasets possible and led to the demand of managing and mining patterns from trajectory datasets to discover objects' movement behavior. As trajectories are generated continuously without limitation and boundaries, they form stream data. Though there are lots of research work done on mining trajectory datasets, none of them considers trajectory data as streams. They treat trajectory data as static data and run multiple scans on the data. In this paper, we present our efforts in facilitating this demand by developing a novel stream data mining algorithm to discover spatio-temporal sequential patterns from trajectories in real time; our algorithm is the first on-line trajectory mining algorithm and only needs to scan the trajectory dataset one time. We also propose a new data structure, called trajectory stream mining tree (TSM-tree), to store and represent up-to-date trajectory patterns. We conduct experiments using real life trajectory datasets to evaluate the performance of our algorithm.
Starting Page 26
Ending Page 32
Page Count 7
File Format PDF
ISBN 9781450325325
DOI 10.1145/2534303.2534313
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-05
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Stream data Spatio-temporal data mining Trajectory pattern
Content Type Text
Resource Type Article
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