By Gerhard Weikum (auth.), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, Chiemi Watanabe (eds.)
This quantity set LNCS 5981 and LNCS 5982 constitutes the refereed lawsuits of the fifteenth overseas convention on Database structures for complicated purposes, 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 equipped in topical sections on P2P-based applied sciences, info mining applied sciences, XML seek and matching, graphs, spatialdatabases, XML applied sciences, time sequence and streams, complicated information mining, question processing, net, sensor networks and communications, details administration, in addition to groups and net graphs. the second one quantity includes contributions regarding trajectories and relocating items, skyline queries, privateness and protection, information streams, similarity seek and occasion processing, garage and complicated themes, business, demo papers, and tutorials and panels.
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Additional info for Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I
Queries delivering and computing, intermediate results transferring, cache and index maintaining. 0GB RAM. All experiments are repeated 10 times, and each of them issues 1000 skyline queries starting from a random node. We distribute the data points randomly into all peers in the BATON network. We use three kinds of different datasets: two synthetic datasets of independent and anticorrelated distribution, which have up to 8 million data points, and a real dataset of the NBA players’ season statistics from 1949 to 2003 , which approximates a correlated data distribution.
In this work, we adopt the latter mechanism in our proposed DCM scheme. Algorithm 1. maxSubRange); if (find Q’s cache index in P ) then cacheIndex =< Q, addr >; RQ =fetchCache(cacheIndex); if (RQ = ∅) then indexPublish(Q, addr); return RQ ; 10 11 else 18 randomly select Qi that Q ⊆ Qi ∧ Q ⊕ Qi ==1; if (Qi exists) then RQi =dcmSkyline(Qi ); if (RQi = ∅) then RQ =skyline(RQi ); indexPublish(Q, addr); return RQ ; 19 else 12 13 14 15 16 17 20 process the skyline query Q; Algorithm 1 shows the process of the skyline query computation based on the enhanced cache utilization strategy.
6. 5 Effect of Updates Finally we show the performance of the algorithms on peer updates. We randomly select 1024 peers and 1 million 6-dimensional data points and vary the peer update rate from 5% to 20% within the period of 1000 query executions. , node join, node leave, data insert, data deletion and data update) is randomly chosen. We use the response time to evaluate the efficiency of different Distributed Cache Indexing for Efficient Subspace Skyline Computation 17 approaches. In Figure 7, the performance of all DCM-based approaches is better than that of the baseline methods, although the DCM mechanism is deteriorated nearly 4 times comparing to the case without updates, as some caches may be disabled due to the updates.