By Christophe Claramunt, Markus Schneider, Raymond Chi-Wing Wong, Li Xiong, Woong-Kee Loh, Cyrus Shahabi, Ki-Joune Li
This publication constitutes the refereed complaints of the 14th overseas Symposium on Spatial and Temporal Databases, SSTD 2015, held in Hong Kong, China, in August 2015.
The 24 revised complete papers including eight demos offered 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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Extra info for Advances in Spatial and Temporal Databases: 14th International Symposium, SSTD 2015, Hong Kong, China, August 26-28, 2015. Proceedings
Ferragina, P. ) ESA 2012. LNCS, vol. 7501, pp. 407–418. Springer, Heidelberg (2012) 19. : Optimizing landmark-based routing and preprocessing. In: CTS: 6th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 5 November 2013, Orlando, FL, USA, p. 25 (2013) 20. : GRASP. extending graph separators for the single-source shortest-path problem. , Wagner, D. ) ESA 2014. LNCS, vol. 8737, pp. 358–370. Springer, Heidelberg (2014) 21. : ReHub. Extending hub labels for reverse k-nearest neighbor queries on large-scale networks (2015).
Tsotras Table 1. 3 GHz RW2 194 120 197 RAM 16 GB RW4 387 419 392 Page Size (a) Size of datasets and indexes 4096 B (b) System specifications Table 2. Parameter optimization on dataset M V1 Contraction Parameter (Time instants) Grid Resolution (Thousand km) 20 40 60 80 20 9295 5884 5162 5779 40 9277 5876 5192 5738 60 9278 5874 5127 5656 80 9260 5815 5146 5413 and calculate the total number of I/Os using this ratio). The speciﬁcations for the system used for the experiments are given in Table 1(b).
Obj FROM forward n1 , knntab n2 WHERE n1 . v = q AND n1 . hub = n2 . hub GROUP BY n2 . obj ORDER BY MIN ( n1 . dist + n2 . 4. kNN query for COLD 1 2 3 4 5 6 7 8 9 10 11 12 SELECT MIN ( n1 . d + n2 . dist ) , UNNEST ( objs ) AS obj FROM ( SELECT UNNEST ( hubs ) AS hub , UNNEST ( dists ) AS d FROM forwcold WHERE v = q ) n1 , /* k - entries per hub , dist */ ( SELECT hub , dist , objs [1: k ] FROM knncold ) n2 WHERE n1 . hub = n2 . hub GROUP BY obj ORDER BY MIN ( n1 . d + n2 . dist ) LIMIT k ; One-to-Many Queries.