In:
ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 3 ( 2023-03-07), p. 113-
Abstract:
In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of location-based services (LBS). Spatio-temporal range query, as the basis of many services, remains a challenge in supporting efficient analysis and calculation of data, especially when large volumes of trajectory data have been accumulated. We propose a Hilbert-GeoSOT spatio-temporal meshing and coding method called HGST to improve the efficiency of spatio-temporal range queries on massive trajectory data. First, the method uses Hilbert to encode the grids obtained based on the GeoSOT space division model, and then constructs a unified time division standard to generate the space–time location identification of trajectory data. Second, this paper builds a novel spatio-temporal index to organize trajectory data, and designs an adaptive spatio-temporal scaling and coding method based on HGST to improve the query performance on indexed records. Finally, we implement a prototype system based on HBase and Spark, and develop a Spark-based algorithm to accelerate the spatio-temporal range query for huge trajectory data. Extensive experiments on a real taxi trajectory dataset demonstrate that HGST improves query efficiency levels by approximately 14.77% and 34.93% compared with GeoSOT-ST and GeoMesa at various spatial scales, respectively, and has better scalability under different data volumes.
Type of Medium:
Online Resource
ISSN:
2220-9964
DOI:
10.3390/ijgi12030113
Language:
English
Publisher:
MDPI AG
Publication Date:
2023
detail.hit.zdb_id:
2655790-3
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