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

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 court cases of the thirteenth overseas Symposium on Spatial and Temporal Databases, SSTD 2013, held in Munich, Germany, in August 2013. The 24 revised complete papers provided have been rigorously reviewed and chosen from fifty eight submissions. The papers are geared up in topical sections on joins and algorithms; mining and discovery; indexing; trajectories and highway community facts; nearest neighbours queries; uncertainty; and demonstrations.

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2 4 8 12 16 20 Number of Candidate Set M (b) Impact of M Fig. 4. Experiment Results: Synthetic Data Impact of M . Performance of our approaches regarding different M values is shown in Figure 4(b). We can see that P, and B perform well in recovering the injected arbitrary shaped region. However, for baseline algorithms, they do not work very well. We can also observe that all approaches are relatively stable regarding changes of M generated during expansion. This is due to the fact that our expansion heuristic always pick the group of candidate grid cells that maximize the score.

Further, DFA becomes slower with as it primarily focuses on the spatial predicate of the k-SDJ. BA manages to combine the above advantages of the score-first paradigm and SFA, and the distance-first paradigm and DFA, as it examines blocks of objects ordered by score and applies the spatial predicate at the block level instead on the whole collections. , |R| = |S| = 5M. S. Qi, P. Bouros, and N. 02 Fig. 7. 02 Fig. 8. 25 Response time (sec) Response time (sec) We also performed a scalability experiment, by joining samples R and S of the ISLES dataset of different sizes |R| and |S|, while setting , k, |P | and λ/|R| to their default values.

The posterior probabilities can be calculated by averaging likelihoods over the distribution of α. 3 Learning Bivariate Poisson Distribution Using EM Algorithm We apply the EM algorithm to learn BP distribution proposed in [8]. BP deals with random variable X = [X1 , X2 ], where X1 = Y0 + Y1 , X2 = Y0 + Y2 , Yi , i ∈ [0, 2] are independent Poisson distribution with mean θi . In our context, q a = θ1 , q b = θ2 and δ = θ0 . We have observations for X1 , X2 but not for Y0 , Y1 and Y2 . Y0 represents the counts of feature a and b occurs in spatial proximity, Y1 and Y2 represent the counts of feature a and b, independently.

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