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- W2034710008 abstract "In this paper, we investigate using specifically-designated spatiotemporal indexing techniques for mining cooccurrence patterns from spatiotemporal datasets with evolving polygon-based representations. Previously, suggested techniques for spatiotemporal pattern mining algorithms did not take spatiotemporal indexing techniques into account. We present a new framework for mining spatiotemporal co-occurrence patterns that can use various indexing techniques for efficiently accessing data. Two well-studied spatiotemporal indexing structures, Scalable and Efficient Trajectory Index (SETI) and Chebyshev Polynomial Indexing are currently implemented and available in our framework." @default.
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- W2034710008 date "2014-10-01" @default.
- W2034710008 modified "2023-09-28" @default.
- W2034710008 title "Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns" @default.
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- W2034710008 doi "https://doi.org/10.1109/bigdata.2014.7004398" @default.
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