By Christophe Claramunt, Markus Schneider, Raymond Chi-Wing Wong, Li Xiong, Woong-Kee Loh, Cyrus Shahabi, Ki-Joune Li

This booklet constitutes the refereed complaints of the 14th foreign Symposium on Spatial and Temporal Databases, SSTD 2015, held in Hong Kong, China, in August 2015.

The 24 revised complete papers including eight demos provided have been conscientiously reviewed and chosen from sixty four submissions. The convention software has the scope on following topics: reachability question and course question, opposite question and indexing, navigation and routing, trajectory research, spatio-temporal ways, privateness and matching, similarity seek and trend, key-phrase and pattern.

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358–370. Springer, Heidelberg (2014) 21. : ReHub. Extending hub labels for reverse k-nearest neighbor queries on large-scale networks (2015). 01497 22. : SALT. a unified framework for all shortestpath query variants on road networks. In: Bampis, E. ) SEA 2015. LNCS, vol. 9125, pp. 298–311. Springer, Heidelberg (2015) 23. : Distance labeling in graphs. In: Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2001, pp. 210–219. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2001) 24.

For the last set of experiments we used the RW1 dataset with dcont = 2 m. Since the contact distance is much smaller than previously, the average contact degree becomes smaller, which in turn leads to longer average time for two objects to reach each other. We start with queries that are 1000 time instants long. We extended the query length up to 8000 time instants (which for this dataset makes about 95 % objects reachable by the end of the query interval). For these experiments, we were not able to optimize the parameters and complete the preprocessing for ReachGrid, since its query processing was very slow (ReachGrid does not scale well under the given scenario).

Moreover, Efentakis et al. [21] have shown how these additional data structures may be constructed from the forward labels in main-memory, requiring less than few seconds, even for the larger tested datasets. k NN Queries. To translate the HL kNN query into SQL, HLDB stores kNN backward labels in a separate DB table denoted knntab that stores triples of the form (hub, dist, obj) (see Table 5). The respective table knntab has the combination of (hub, dist, obj) as a composite primary key and is clustered according to those columns.

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