Treffer: Scalable management of complex spatial data types ; Κλιμακώσιμη διαχείριση σύνθετων χωρικών τύπων δεδομένων

Scalable spatial data management is crucial in both scientific and commercial domains, particularly in Geographic Information Systems (GIS), which handle massive volumes of geographic data. As spatial data continues to grow rapidly, the demand for efficient spatial data analytics tools has become increasingly pressing. A core functionality of such tools is the computation of spatial and topological joins over large collections of objects. These operations aim to identify intersecting object pairs (i.e., objects that share at least one common point), a fundamental task with applications in geospatial interlinking, spatial databases, and beyond. However, intersection testing is computationally intensive, especially for polygonal objects, which often contain a large number of vertices and require costly geometric processing.This dissertation investigates approximation techniques for handling high-complexity polygons, with the aim of making processing faster and more efficient. The central objective is to minimize reliance on original geometries, using them for computations only as a last resort. Our proposed solutions introduce efficient polygon approximation methods with a low memory footprint, along with filtering techniques that enable spatial joins to be evaluated without directly accessing the original geometries. The work addresses both scalability and accuracy challenges while striving to deliver solutions that are directly applicable to modern in-memory spatial databases.Scalable spatial data management has two key aspects. First, query processing algorithms must be highly parallelizable and independent, enabling them to fully leverage distributed and parallel spatial databases for both vertical and horizontal scalability. Second, they must maintain efficiency as geometric complexity increases, since complex shapes often become a major bottleneck in spatial query processing. In the second part of this dissertation, we design and implement a prototype distributed spatial data management framework that ...