Elasticsearch for big geotemporal data

• Дата: 2025
• Автори: Zhyrenkov, O.S., Doroshenko, A.Yu.
• Формат: Стаття
• Мова: English
• Опубліковано: PROBLEMS IN PROGRAMMING 2025
• Теми: Elasticsearch; geospatial data; distributed architecture; H3 indexing; BKD tree; R-tree; performance optimization; geotemporal data; trajectories; UDC 004.65; 004.652; 004.657; 004.78
• Онлайн доступ: https://pp.isofts.kiev.ua/index.php/ojs1/article/view/764
• Назва журналу: Problems in programming

An exponential growth in the volume and complexity of geospatial data, driven by advances in GPS technology, mobile devices, and Internet of Things (IoT) sensors, has created an urgent need for scalable and efficient solutions for storage and query processing [1]. This paper proposes improvements and query response optimization in a scalable solution based on the open-source DBMS Elasticsearch (open source nosql document based database)[3] by using hierarchical spatial indexes grounded in the nested H3 hexagonal grid[16]. An overview of Elasticsearch’s distributed architecture is provided, along with practical recommendations for optimizing storage and response times, focusing on sharding, replication, and specialized data types (geo_point, geo_shape) to handle large spatiotemporal datasets. Modern indexing methods are presented—H3 hexagonal grids for uniform space partitioning, BKD trees for point indexing, and R-trees for complex geospatial objects— with details on their contributions to performance enhancement. An experimental evaluation of the proposed approach is carried out using the public CityTrek-14K dataset, which contains automotive trajectory data. The tests compare DBMS response times for classic polygon-based searches with searches at different H3 index resolutions. The results confirm that high-resolution indexing significantly reduces query times while balancing accuracy and resource usage. Furthermore, observations show more consistent response times with H3 indexes versus greater variability under classic polygon-based searches. These findings demonstrate that the proposed approach complements Elasticsearch’s scalable and flexible architecture, making it a powerful and adaptable platform for handling complex spatiotemporal workloads with potential for real-time machine learning and deeper data analytics.Prombles in programming 2025; 1: 55-62