{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:41Z","timestamp":1760146241822,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources","award":["KF-2023-08-25","42372345"],"award-info":[{"award-number":["KF-2023-08-25","42372345"]}]},{"name":"the National Natural Science Foundation of China","award":["KF-2023-08-25","42372345"],"award-info":[{"award-number":["KF-2023-08-25","42372345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Spatiotemporal big data contain information in multiple dimensions such as space and time. Spatiotemporal data have the characteristics of large volume, intricate spatiotemporal relationship, and uneven spatiotemporal distribution. Index structure is one of the most important technologies used to improve system data analysis and workload. However, it is difficult to dynamically adjust with data density, resulting in increased maintenance costs and retrieval complexity. At the same time, maintaining the proximity of spatiotemporal data in spatial or temporal dimensions is crucial for efficient spatiotemporal analysis. To address these challenges, this paper proposes a learned index method, GLMI (Geohash and piecewise linear model-based index for spatiotemporal data). GLMI uses dynamic space partitioning based on the Hilbert curve to reduce the impact of data skew on index performance. In the time dimension, a piecewise linear model was constructed using the ShrinkingCone algorithm, and a buffer was designed to support the fast writing of spatiotemporal data. Compared with the current mainstream traditional high-dimensional indexes and the ZM index, GLMI has a smaller space consumption and shorter construction time compared to high-dimensional learned indexes on real traffic itinerary and trajectory record datasets. Meanwhile, GLMI also has an advantage in query efficiency.<\/jats:p>","DOI":"10.3390\/a17110474","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T04:10:14Z","timestamp":1729570214000},"page":"474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GLMI: An Efficient Spatiotemporal Index Leveraging Geohash and Piecewise Linear Models for Optimized Query Performance"],"prefix":"10.3390","volume":"17","author":[{"given":"Kun","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China"},{"name":"Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Intelligent Geological Resources and Environment Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China"},{"name":"Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Intelligent Geological Resources and Environment Technology, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genshen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China"},{"name":"Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Intelligent Geological Resources and Environment Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengping","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China"},{"name":"Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Intelligent Geological Resources and Environment Technology, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3052-9223","authenticated-orcid":false,"given":"Qiyu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, China"},{"name":"Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Intelligent Geological Resources and Environment Technology, Wuhan 430074, China"},{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"ref_1","first-page":"164","article-title":"Data model for geological spatiotemporal big data expression and storage management","volume":"39","author":"Liu","year":"2020","journal-title":"Bull. 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