Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Current methods for similarity search of trajectories  are inefficient in terms of search time and memory when applied to massive datasets. In this paper, we address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT) . tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure . We also present two novel techniques of node reduction and a space-efficient representation for tries, which enable to dramatically enhance a memory efficiency of tries . We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly with respect to search time and memory efficiency .
Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Locality sensitive hashing (LSH) is a powerful technique for fast similarity searches. Recent methods employ LSH and attempt to realize an efficient similarity search of trajectories ; however, those methods are inefficient in terms of search time and memory when applied to massive datasets. To address this problem , we present the trajectory-indexing succinct trit-array trie (tSTAT) , which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie . We also present two novel techniques that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is a space-efficient representation that leverages the idea behind succinct data structures (i.e., a compressed data structure supporting fast data operations) . We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly in comparison to state-of-the-art similarity search methods .
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Current methods for similarity
Locality sensitive hashing (LSH) is a powerful technique for fast similarity searches. Recent methods employ LSH and attempt to realize an efficient similarity
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; however, those methods
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In this paper, we
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by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call
, we present the
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. tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure
, which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie
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of node reduction and
that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is
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for tries, which enable to dramatically enhance a memory efficiency of tries
that leverages the idea behind succinct data structures (i.e., a compressed data structure supporting fast data operations)
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with respect to search time and memory efficiency
in comparison to state-of-the-art similarity search methods