首页|Discovering Companion Vehicles from Live Streaming Traffic Data
Discovering Companion Vehicles from Live Streaming Traffic Data
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NETL
Springer Nature
Companions of moving objects are object groups that move together in a period of time。 To quickly identify companion vehicles from a special kind of streaming traffic data， called Automatic Number Plate Recognition (ANPR) data， this paper proposes an approach to discover companion vehicles。 Compared to related approaches， we transform the companion discovery into a frequent sequence-mining problem。 We make several improvements on top of a recent frequent sequence-mining algorithm， called SeqStream， to handle customized time constraints among sequence elements when discovering traveling companions。 We also use pseudo projection technique to improve the performance of our algorithm。 Finally， extensive experiments are done using a real dataset to show efficiency and effectiveness of our approach。
Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, China,Cloud Computing Research Center, North China University of Technology,Beijing, China
Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing, China,Cloud Computing Research Center, North China University of Technology,Beijing, China,School of Computer Science and Technology, Tianjin University,Tianjin, China
International workshop on spatial-temporal data management and analytics;Asia-Pacific web conference on Web technologies and applications;International workshop on web data mining and applications;International workshop on graph analytics and query process
NETL
Springer Nature
