Download Advances in Spatial and Temporal Databases: 13th by Shuyao Qi, Panagiotis Bouros, Nikos Mamoulis (auth.), Mario PDF

By Shuyao Qi, Panagiotis Bouros, Nikos Mamoulis (auth.), Mario A. Nascimento, Timos Sellis, Reynold Cheng, Jörg Sander, Yu Zheng, Hans-Peter Kriegel, Matthias Renz, Christian Sengstock (eds.)

This booklet constitutes the refereed complaints of the thirteenth overseas Symposium on Spatial and Temporal Databases, SSTD 2013, held in Munich, Germany, in August 2013. The 24 revised complete papers awarded have been rigorously reviewed and chosen from fifty eight submissions. The papers are prepared in topical sections on joins and algorithms; mining and discovery; indexing; trajectories and street community info; nearest neighbours queries; uncertainty; and demonstrations.

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Figure 6 compares the three approaches, showing that their response time is affected only by the increase of the distance and not by k or |P |; this is due to the fact that all methods primarily focus on the spatial predicate of the k-SDJ, which is independent of the scores. Due to its ability to use score aggregation bounds, DFA not only outperforms the other methods (in some cases for more than two orders of magnitude), but it is also very little affected by the increase of . 3, and alternatives based on plane sweep and R-tree join.

They argue that identifying the dependence relationship of spatial features and computing the prevalence measure of features have different distributions in different areas of the global space. For this purpose, they postpone the determination of neighbor relations to the prevalence measure computation step and a greedy algorithm is proposed to find co-location patterns with different neighbors constraints in different areas. A statistical model for co-location that considers auto-correlation and feature-abundance effect was recently discussed in [2].

Bivariate Poisson distribution was widely used to model the count of disease in epidemics. In this work, our Bayesian statistics based approach uses the bivariate Poisson 36 S. Wang, Y. S. Wang distribution and Bayesian spatial scan statistic to discover arbitrarily shaped regions with co-location patterns. 7 Conclusion In this paper, we studied the problem of finding regional co-location with arbitrary shapes. For this purpose, we proposed two approaches: frequentist method and Bayesian statistics.

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