Get Advances in Spatial and Temporal Databases: 13th 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.)

ISBN-10: 3642402348

ISBN-13: 9783642402340

ISBN-10: 3642402356

ISBN-13: 9783642402357

This ebook 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 conscientiously 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 info; nearest neighbours queries; uncertainty; and demonstrations.

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Read or Download Advances in Spatial and Temporal Databases: 13th International Symposium, SSTD 2013, Munich, Germany, August 21-23, 2013. Proceedings PDF

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Extra resources for Advances in Spatial and Temporal Databases: 13th International Symposium, SSTD 2013, Munich, Germany, August 21-23, 2013. Proceedings

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S. Wang Estimating Parameters and Choosing Prior To choose prior, we follow the framework proposed in [11]. We assume that we know the prior probability of a co-location outbreak p. Then P (H0 ) = 1 − p. We also assume the probability of the outbreak is equally distributed to all regions. So, P (H1 (S)) = NpS where Ns is the number of possible arbitrarily shaped regions. Since we don’t know Ns , we use the number of rectangular regions as an approximation. For any given region S, we assume that δin is the occurrence rate of a, b together inside S.

Efficient Top-k Spatial Distance Joins 13 Table 2. 005 BA algorithms. 3 Ghz Intel Core i7 CPU with 8GB of RAM running OS X. 68M locations associated with photographs taken from the city of London, UK over a period of 2 years and hosted on the Flickr photosharing website, and (2) ISLES that contains 20M POIs in the area of the British isles drawn from the OpenStreetMap project dump. To perform the experiments, every collection is split into two equally sized parts denoted by R and S. This is to avoid performing a self-join which will produce result pairs involving exactly the same object.

In D we have spatial feature a occurring at a set of discrete spatial locations La and spatial feature b occurring at another set of discrete spatial locations Lb (La and Lb may overlap). We also have two baseline location sets B a and B b which represent the possible locations where these two features can occur, respectively. B a or B b may correspond to the underlying locations that can host the occurrence of a feature. For example, if a is one type of disease, then B a will be the base population that may be infected by the disease.

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Advances in Spatial and Temporal Databases: 13th International Symposium, SSTD 2013, Munich, Germany, August 21-23, 2013. Proceedings 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.)


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