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Journal of Computer and System Sciences - 18th Annual ACM Symposium on Theory of Computing (STOC), May 28-30, 1986
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Access methods for multiversion data
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A model for the prediction of R-tree performance
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A foundation for representing and querying moving objects
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Overlapping linear quadtrees and spatio-temporal query processing
The Computer Journal
R-trees: a dynamic index structure for spatial searching
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STR: A Simple and Efficient Algorithm for R-Tree Packing
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SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
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Cost Models for Overlapping and Multi-Version B-Trees
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Indexing of Moving Objects for Location-Based Services
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Trajectory queries and octagons in moving object databases
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Indexing of network constrained moving objects
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Indexing multi-dimensional time-series with support for multiple distance measures
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Analysis of predictive spatio-temporal queries
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Journal of Computer Science and Technology
Querying about the Past, the Present, and the Future in Spatio-Temporal Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
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Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Spatiotemporal Aggregate Computation: A Survey
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Indexing the Trajectories of Moving Objects in Networks*
Geoinformatica
Historical spatio-temporal aggregation
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Trajectory Indexing Using Movement Constraints
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Efficient trajectory joins using symbolic representations
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Complex spatio-temporal pattern queries
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A trajectory splitting model for efficient spatio-temporal indexing
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Close pair queries in moving object databases
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Time relaxed spatiotemporal trajectory joins
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Modeling Multiple Time Series for Anomaly Detection
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Global distance-based segmentation of trajectories
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Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations
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KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
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IEICE - Transactions on Information and Systems
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Spatiotemporal objects i.e., objects which change their position and/or extent over time, appear in many applications. This paper addresses the problem of indexing large volumes of such data. We consider general object movements and extent changes. We further concentrate on "snapshot" as well as small "interval" historical queries on the gathered data. The obvious approach that approximates spatiotemporal objects with MBRs and uses a traditional multidimensional access method to index them is inefficient. Objects that "live" for long time intervals have large MBRs which introduce a lot of empty space. Clustering long intervals has been dealt in temporal databases by the use of partially persistent indices. What differentiates this problem from traditional temporal indexing is that objects are allowed to move/change during their lifetime. Better methods are thus needed to approximate general spatiotemporal objects. One obvious solution is to introduce artificial splits: the lifetime of a long-lived object is split into smaller consecutive pieces. This decreases the empty space but increases the number of indexed MBRs. We first introduce two algorithms for splitting a given spatiotemporal object. Then, given an upper bound on the total number of possible splits, we present three algorithms that decide how the splits should be distributed among the objects so that the total empty space is minimized.