TY - CHAP A1 - Heinz, Florian A1 - Schildgen, Johannes T1 - A Data Model and Operations for Higher-Dimensional Moving Objects in Databases T2 - EMODE '23: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives 2023, Hamburg, 23 November 2023 N2 - Moving Object Databases are designed to store and process database objects with attributes that can change over time. Simple examples are moving points, that change position over time, a bit more complex are moving regions, that can also change shape. The spatial and spatiotemporal object types in current moving objects databases are limited to two dimensions. This work strives to extend the set of spatial moving object types into the third and even higher dimensions while preserving a consistent family of operations for it. A robust algorithm for the interpolation of two regions to a moving region of any dimensionality is developed, as well as the fundamental ideas for several other operations. KW - moving objects KW - spatiotemporal data KW - data models KW - moving regions Y1 - 2023 SN - 979-8-4007-0347-8 U6 - https://doi.org/10.1145/3615885.3628006 SP - 30 EP - 39 PB - ACM ER - TY - CHAP A1 - Heinz, Florian T1 - Creating Moving Regions from Satellite Scan Data T2 - SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, October 29 - November 1, 2024, Atlanta, GA, USA N2 - Moving objects databases are specialized on storing and processing objects with spatial attributes that change over time. An important class of objects are moving regions that can change shape and position over time. Currently, the prevailing method for constructing moving regions is to interpolate them from snapshots. In this paper, an alternative method is proposed, that is better suitable for certain kinds of data as, for example, satellite scans of the earth surface. For instance, the NASA provides datasets with active fire data in near real-time from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor, attached to satellites as the Suomi NPP and NOAA-20. These satellites orbit earth about 14 times a day and provide full global coverage twice a day. Using these satellites, datasets are compiled that provide locations of active fires on earth as lists of positions and timestamps. This paper describes an alternative, better suitable method for creating moving regions from these lists, which can then be imported into and processed by moving objects databases. KW - spatial data KW - spatiotemporal data KW - moving objects KW - remote sensing Y1 - 2024 SN - 979-8-4007-1107-7 U6 - https://doi.org/10.1145/3678717.3691214 SP - 641 EP - 644 PB - ACM CY - New York, NY, USA ER -