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• W2023843551 abstract "The wide adaptation of GPS and cellular technologies has created many applications that collect and maintain large repositories of data in the form of trajectories. Previous work on querying/analyzing trajectorial data typically falls into methods that either address spatial range and NN queries, or, similarity based queries. Nevertheless, trajectories are complex objects whose behavior over time and space can be better captured as a sequence of interesting events. We thus facilitate the use of motion pattern queries which allow the user to select trajectories based on specific motion patterns. Such patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. Moreover, we are interested in flexible patterns that allow the user to include variables in the query pattern and thus greatly increase its expressive power. In this paper we introduce a framework for efficient processing of flexible pattern queries. The framework includes an underlying indexing structure and algorithms for query processing using different evaluation strategies. An extensive performance evaluation of this framework shows significant performance improvement when compared to existing solutions." @default.
• W2023843551 created "2016-06-24" @default.
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• W2023843551 date "2010-03-22" @default.
• W2023843551 modified "2023-09-24" @default.
• W2023843551 title "Querying trajectories using flexible patterns" @default.
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• W2023843551 doi "https://doi.org/10.1145/1739041.1739091" @default.
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