By M. Tamer Özsu (auth.), Qing Li, Ling Feng, Jian Pei, Sean X. Wang, Xiaofang Zhou, Qiao-Ming Zhu (eds.)
This e-book constitutes the lawsuits of the joint overseas convention APWeb/WAIM 2009 which used to be held in Suzhou, China, in the course of April 1-4, 2009.
The forty two complete papers provided including 26 brief papers and the abstracts of two keynote speeches have been conscientiously reviewed and chosen for inclusion within the publication. the subjects coated are question processing, topic-based innovations, internet facts processing, multidimensional info research, circulation info processing, facts mining and its purposes, and knowledge administration aid to complex applications.
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Additional info for Advances in Data and Web Management: Joint International Conferences, APWeb/WAIM 2009 Suzhou, China, April 2-4, 2009 Proceedings
Table 8 shows a database over which algorithm NRA performs only 6 sorted accesses to depth 3, outputs R1 as the top 1 object while algorithm SNRA sorted accesses the whole L1 list and totally does n + 3 sorted accesses. (After depth [1, 1], R2 is the best competitor, so we sorted access to depth [2, 1], then R2 is still the best competitor, then sorted access to depth [3, 1], at this depth, R3 is the best competitor, then sorted access to depth [3, 2], R2 becomes the best competitor since R3 ’s exact value is less than R2 ’s worst value, then R2 will be the best competitor until the end of L1 at depth [n − 1, 2].
D) Return the objects in Tk . 3 Selective-NRA Algorithms In this section we will propose our Selective-NRA algorithms. In the rest of this section we ﬁrst give an equivalent form of algorithm NRA’s stopping rule, and introduce some lemmas and observations which motivate us to propose algorithm SNRA; secondly we will show our algorithm SNRA and prove its correctness; ﬁnally we will propose an instance optimal algorithm based on algorithm SNRA. 1 Observations of NRA Algorithm Definition 1. Call an object R “best competitor” if R has the largest “best value” (B d (R)) among all “viable”1 objects which are not in the current top-k.
The moving speed of each object is uniformly distributed in between 0 and 20 m/sec. When an object o reaches node n in a network, the next edge on which o moves is randomly chosen from the edges connecting n. In the experimental space, we also generate 30 query objects whose speeds are in the same range as the moving objects mentioned above. Similarly, the next edge that the query object moves on is randomly selected once it reaches a network node. , K) and the length of query time interval are 20 and 100 time units, respectively.