The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
An optimal algorithm for intersecting line segments in the plane
Journal of the ACM (JACM)
Equivalence of Relational Algebra and Relational Calculus Query Languages Having Aggregate Functions
Journal of the ACM (JACM)
Progressive approximate aggregate queries with a multi-resolution tree structure
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Improving min/max aggregation over spatial objects
Proceedings of the 9th ACM international symposium on Advances in geographic information systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
COSIT '97 Proceedings of the International Conference on Spatial Information Theory: A Theoretical Basis for GIS
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
Topological relationships between complex spatial objects
ACM Transactions on Database Systems (TODS)
Algorithms for Reporting and Counting Geometric Intersections
IEEE Transactions on Computers
Extracting moving regions from spatial data
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Aggregate operators are a useful class of operators in relational databases. In this paper, we examine spatial aggregate operators over regions. Spatial aggregates are defined to operate over a set of regions, and return a single region as a result. We systematically identify individual spatial aggregate operations by extending existing spatial operations into aggregate form. Semantic meaning for each operator is defined over a specified data model. Once defined, algorithms for computing spatial aggregates over regions are provided. We show that all proposed aggregates can be computed using a single algorithm. Furthermore, we provide serial and parallel algorithm constructions that can take advantage of vector co-processors, such as graphical processing units (GPUs), and that can be integrated into map/reduce queries to take advantage of big data-style clusters. Example queries and their results are provided.