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Intelligent systems. Theory and applications, 2021, Volume 25, Issue 1, Pages 71–88 (Mi ista295)  

Part 2. Special Issues in Intellectual Systems Theory

Distributed companion trajectory search algorithm

A. Sokolov, P. A. Aliseichik, S. V. Moiseev

Lomonosov Moscow State University
References:
Abstract: The work is focused on development of the distributed algorithm for search of companion-trajectories. We call two trajectories as companion-trajectories if they both contain section of required duration on which at any time moment distance between two objects is no larger than specified threshold. It's assumed that search is performed on database that is so big that it cannot be stored and processed on a single work station. Such tasks are usually called big-data tasks. We define trajectory quantization procedure, introduce cell index data structure, define similar trajectories. Then we build effective algorithm for search of similar trajectories. Based on this algorithm we develop effective local and distributed algorithms for search of companion trajectories. Asymptotic complexity of these algorithms is estimated.
Keywords: big-data, geo-spatial data analysis, trajectory analysis, companion-trajectory search, similar trajectory search, traffic analysis.
Document Type: Article
Language: Russian
Citation: A. Sokolov, P. A. Aliseichik, S. V. Moiseev, “Distributed companion trajectory search algorithm”, Intelligent systems. Theory and applications, 25:1 (2021), 71–88
Citation in format AMSBIB
\Bibitem{SokAliMoi21}
\by A.~Sokolov, P.~A.~Aliseichik, S.~V.~Moiseev
\paper Distributed companion trajectory search algorithm
\jour Intelligent systems. Theory and applications
\yr 2021
\vol 25
\issue 1
\pages 71--88
\mathnet{http://mi.mathnet.ru/ista295}
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