Advances in spatial databases

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The content covers a variety of topics related to spatio-temporal database systems and spatial data management. It includes invited talks and explores topological queries, research issues, and multi-resolution approaches. The discussion extends to generalizing graphs through amalgamation and selection, as well as data structures for simplicial multi-complexes. Key aspects of spatial indexing are highlighted, including XZ-ordering as a space-filling curve for spatially extended objects and a generalized R-Tree bulk-insertion strategy. The text addresses the challenges of moving objects and spatio-temporal data, emphasizing the need to capture uncertainty in moving-object representations. It also delves into dynamic spatial clustering for mobile information sharing, the generation of spatiotemporal datasets, and spatial data mining and classification techniques. Efficient polygon amalgamation methods for spatial OLAP and proximity relationship matching in databases are discussed. The annotation includes methodologies for multi-way spatial joins using R-Trees, algorithms for joining R-Trees and quadtrees, and performance evaluations of spatial join processing strategies. Additionally, it touches on uncertainty management in spatial databases and applications in industrial and visionary contexts, including experiences with extensible databases and requirements for traffic telematics.