Succinct Trit-array Trie for Scalable Trajectory Similarity Search
This work addresses the problem of scalable trajectory similarity search for researchers and industries handling large mobility datasets, representing an incremental improvement over prior LSH-based methods.
The paper tackles the inefficiency of existing locality sensitive hashing (LSH) methods for trajectory similarity search on massive datasets by introducing tSTAT, a scalable method that uses a succinct trit-array trie, resulting in superior performance in search time and memory compared to state-of-the-art methods.
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.