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Trajectory similarity computation is a fundamental component in a variety ofreal-world applications, such as ridesharing, road planning, and transportationoptimization. Recent advances in mobile devices have enabled an unprecedentedincrease in the amount of available trajectory data such that efficient queryprocessing can no longer be supported by a single machine. As a result, meansof performing distributed in-memory trajectory similarity search are calledfor. However, existing distributed proposals suffer from either computingresource waste or are unable to support the range of similarity measures thatare being used. We propose a distributed in-memory management framework calledREPOSE for processing top-k trajectory similarity queries on Spark. We developa reference point trie (RP-Trie) index to organize trajectory data for localsearch. In addition, we design a novel heterogeneous global partitioningstrategy to eliminate load imbalance in distributed settings. We report onextensive experiments with real-world data that offer insight into theperformance of the solution, and show that the solution is capable ofoutperforming the state-of-the-art proposals.

REPOSE: Distributed Top-k Trajectory Similarity Search with Local Reference Point Tries