By Nan Chen, Lidan Shou, Gang Chen, Ke Chen, Yunjun Gao (auth.), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, Chiemi Watanabe (eds.)
This quantity set LNCS 5981 and LNCS 5982 constitutes the refereed complaints of the fifteenth foreign convention on Database structures for complicated functions, DASFAA 2010, held in Tsukuba, Japan, in April 2010. The 39 revised complete papers and sixteen revised brief papers awarded including three invited keynote papers, 22 demonstration papers, 6 business papers, and a couple of keynote talks have been conscientiously reviewed and chosen from 285 submissions. The papers of the 1st quantity are geared up in topical sections on P2P-based applied sciences, facts mining applied sciences, XML seek and matching, graphs, spatial databases, XML applied sciences, time sequence and streams, complicated info mining, question processing, internet, sensor networks and communications, details administration, in addition to groups and net graphs. the second one quantity comprises contributions concerning trajectories and relocating items, skyline queries, privateness and defense, information streams, similarity seek and occasion processing, garage and complicated themes, commercial, demo papers, and tutorials and panels.
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Extra info for Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II
In leaf nodes, each entry has a pointer to a cluster and the IDs of roads occurring in that cluster. In internal nodes, each entry has a pointer to a child node and the union of roads IDs in its child node. Figure 4 shows an example C-tree. Given a new trajectory T rj, starting from the root of the C-tree, we calculate the similarity between T rj and every entry’s RID in the node by using the following similarity function. Simc (T rj, RID) = |S(T rj) ∩ RID| |S(T rj)| (3) Simc computes the percentage of common roads included in T rj and RID, where S(T rj) denotes the set of road IDs in trajectory T rj.
Therefore, in the index, the moving objects may have diﬀerent α. According to Theorem 1, β of the B s -tree should be equal to the biggest α of all moving objects to guarantee the query correctness. Bigger β is meaningless, as it increases query costs. If all moving objects in the B s -tree have the same α, we call the Bs -tree is in a steady state, and β = α. Also note that, there is some restriction for the longest update interval of a moving object. In the B s -tree, the time interval T m should be larger than most t per s of all moving objects in the system.
In this paper we choose n = 3, as the previous experiments of B+-based-tree of moving objects in  show that it is an appropriate balance value. 2 Self-tuning Framework From the above analysis, we find that the update and query performance of the B s -tree can be tuned by the update parameter α and the query parameter β. However, α and β can not be changed arbitrarily. We should guarantee the correctness of the queries. Theorem 1. As long as β is no less than all α of all moving objects, the query correctness of the Bs -tree can be guaranteed.
Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II by Nan Chen, Lidan Shou, Gang Chen, Ke Chen, Yunjun Gao (auth.), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, Chiemi Watanabe (eds.)