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Abstract
As the volume and complexity of spatiotemporal data continue to expand rapidly across various domains such as urban planning, environmental monitoring, and logistics, the demand for comprehensive data management systems becomes increasingly urgent. Handling such data entails intricate topological and analytical operations, emphasizing the necessity for robust and adaptable solutions capable of addressing diverse user queries. This article introduces Distributed MobilityDB, 1 an open source system engineered to manage big spatiotemporal trajectory datasets within SQL environments. Distributed MobilityDB offers capabilities for scalable spatiotemporal data management, facilitating efficient distributed query processing while seamlessly integrating with existing MobilityDB SQL operations. Key contributions highlighted in the article encompass an adaptive spatiotemporal SQL query engine. This engine channels user SQL queries through various planning strategies for optimizing the distributed query plan, then distributing the query execution across cluster nodes transparently to the user. Various spatiotemporal query types are supported for distribution, including range selections, and joins proximity. Distributed MobilityDB is implemented as an add-on extension to PostgreSQL, which facilitates installing it on a readily running server. The article further presents extensive experiments conducted on both cloud and on-premise environments using both real and synthetic datasets, including the Automatic Identification System for ship trajectories and BerlinMOD for simulated person trips.
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