Diana Baumann

Diana Baumann, Tim C. Rese, David Bermbach

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.

Tim C. Rese, Nils Japke, Diana Baumann et al.

The rapid growth of spatiotemporal data volumes needs to be handled by database systems capable of efficiently managing and querying such data. Existing systems such as PostGIS, SpaceTime, and MobilityDB offer partial solutions but differ widely in scope and performance. Also, first spatiotemporal benchmarks provide valuable insights but are limited in scope and, to our knowledge, no application-centric benchmarking suite exists. In this paper, we propose GeoBenchr, an open-source, application-centric benchmarking suite for spatiotemporal platforms. GeoBenchr enables comprehensive evaluation across diverse datasets, query types, and workload patterns, reflecting realistic use cases from domains such as cycling, aviation, and maritime tracking. We use our GeoBenchr prototype to evaluate several system aspects including scalability, configuration impact, and cross-platform performance comparison. Our results highlight the importance of application-centric benchmarking in selecting suitable spatiotemporal database systems for real-world scenarios.

Diana Baumann, Nils Japke, Tim C. Rese et al.

Mobility data science offers insights into the complex interconnections of spatial data of moving objects and their surroundings, often based on a combination of vector and raster data. For example, mobility traces are usually in vector format, weather data are often in raster format. Yet, available spatial analysis tools for exploratory data science push data scientists towards one or the other, providing only limited support for the respective other. In this paper, we contribute to this problem space with a value-based quadtree index, which serves as a bridge builder to support joint spatial analysis on vector and raster data leveraging their unique autocorrelation property. We achieve a 90% reduction in median Point-in-Polygon query latency, while keeping the accuracy of query responses at equal level.