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arXiv · 2507.06005

Towards Serverless Processing of Spatiotemporal Big Data Queries

Diana Baumann·Tim C. Rese·David Bermbach
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Abstract

Spatiotemporal data are being produced in continuously growing volumes by a variety of data sources and a variety of application fields rely on rapid analysis of such data. Existing systems such as PostGIS or MobilityDB usually build on relational database systems, thus, inheriting their scale-out characteristics. As a consequence, big spatiotemporal data scenarios still have limited support even though many query types can easily be parallelized. In this paper, we propose our vision of a native serverless data processing approach for spatiotemporal data: We break down queries into small subqueries which then leverage the near-instant scaling of Function-as-a-Service platforms to execute them in parallel. With this, we partially solve the scalability needs of big spatiotemporal data processing.

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Diana Baumann, Tim C. Rese, David Bermbach. 2026-05-20. Towards Serverless Processing of Spatiotemporal Big Data Queries. https://doi.org/10.1109/ic2e65552.2025.00015

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