{"ID":2897067,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.06005","arxiv_id":"2507.06005","title":"Towards Serverless Processing of Spatiotemporal Big Data Queries","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.06005","url_pdf":"https://arxiv.org/pdf/2507.06005v3","authors":"[\"Diana Baumann\",\"Tim C. Rese\",\"David Bermbach\"]","published":"2025-07-08T14:08:30Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.DC\"]","methods":"[]","has_code":false}