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- W2768531189 abstract "With the development of positioning technologies and the increasing popularity of location-aware devices, large volumes of trajectory data have been accumulated. However, efficient management and access to massive trajectory data remains a big challenge. The emerging NoSQL database has provided a promising solution for this challenge. But most of the current NoSQL databases do not support direct spatiotemporal indexing of massive trajectory data. This paper presents a novel trajectory indexing method to accelerate time-consuming spatiotemporal queries of massive trajectory data. This method extends the widely-used GeoHash algorithm to satisfy the requirements for both high-frequent updates and common trajectory query operations, e.g. exact point query and spatiotemporal range query. This ST-Hash index was implemented and evaluated in a NoSQL database (MongoDB). Experimental results show that this proposed ST-Hash index can greatly improve the query performance and exhibits robust performance scalability over different input data sizes." @default.
- W2768531189 created "2017-12-04" @default.
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- W2768531189 date "2017-08-01" @default.
- W2768531189 modified "2023-09-26" @default.
- W2768531189 title "ST-hash: An efficient spatiotemporal index for massive trajectory data in a NoSQL database" @default.
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- W2768531189 doi "https://doi.org/10.1109/geoinformatics.2017.8090927" @default.
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