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The Quadtree and Related Hierarchical Data Structures
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SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Managing Intervals Efficiently in Object-Relational Databases
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SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
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ACM SIGMOD Record
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WISE'10 Proceedings of the 11th international conference on Web information systems engineering
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DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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The VLDB Journal — The International Journal on Very Large Data Bases
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Despite pressing need, current relational database management systems (RDBMS) support for spatio-temporal data is limited and inadequate, and most existing spatio-temporal indices cannot be readily integrated into existing RDBMSs. This paper proposes a practical index for spatio-temporal (PIST) data, an indexing technique, rather than a new indexing structure, for historical spatio-temporal data points that can be fully integrated within existing RDBMSs. PIST separates the spatial and temporal components of the data. For the spatial component, we develop a formal cost model and a partitioning strategy that leads to an optimal space partitioning for uniformly distributed data and an efficient heuristic partitioning for arbitrary data distributions. For the temporal component of the data a B驴+驴-tree is used. We show that this layer's performance can be maximized if an optimal maximal temporal range is enforced, and we present a procedure to determine such an optimal value. Being fully mapped onto a RDBMS, desirable and important properties, such as concurrency control, are immediately inherited by PIST. Using ORACLE as our implementation platform we perform extensive experiments with both real and synthetic datasets comparing its performance against other RDBMS-based options, as well as the MV3R-tree. PIST outperforms the former by at least one order of magnitude, and is competitive or better with respect to the latter, with the unarguable advantage that it can readily used on top of virtually any existing RDBMS.