Download Advances in Data and Web Management: Joint International by M. Tamer Özsu (auth.), Qing Li, Ling Feng, Jian Pei, Sean X. PDF

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 court cases of the joint overseas convention APWeb/WAIM 2009 which was once held in Suzhou, China, in the course of April 1-4, 2009.

The forty two complete papers offered including 26 brief papers and the abstracts of two keynote speeches have been rigorously reviewed and chosen for inclusion within the booklet. the themes lined are question processing, topic-based thoughts, net facts processing, multidimensional facts research, movement info processing, info mining and its functions, and knowledge administration help to complex applications.

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Extra resources for Advances in Data and Web Management: Joint International Conferences, APWeb/WAIM 2009 Suzhou, China, April 2-4, 2009 Proceedings

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2. Access cost over normal database as the aggregation function which was a most common one. For Hybrid-SNRA we set p=11 as default. We used both synthetic and real-world data to evaluate SNRA, Hybrid-SNRA and NRA algorithms. 1 Evaluation for Synthetic Data We conducted experiments on three synthetic data sets with different distributions. They are uniform distributed, normal (Gaussian) distributed and exponential distributed. We set n = 100000, m = 2, 4, . . , 12, and k = 20. Figure 1-3 show that on the uniform, normal and exponential distributed databases our algorithm SNRA as well as Hybrid-SNRA performs fewer sorted accesses than algorithm NRA, and algorithm SNRA performs the fewest sorted accesses among these three algorithms.

However, if the IMA algorithm is adopted to answer a CKNN query, some of the CKNN Query over Moving Objects in Road Networks CKNN IMA(UI=5) IMA(UI=10) CKNN IMA(UI=5) 37 IMA(UI=10) 200 100 80 Precision(%) CPU time(ms) 150 100 60 40 50 20 0 10 30 50 70 0 100 10 Query interval length(time units) 30 50 70 100 Query interval length(time units) (a) CPU time (b) Precision Fig. 6. Effect of query interval length KNN results are unknown due to the nature of discrete location updates. As we can see, the precision of IMA(U I = 5) can only reach 40% to 60%.

Yuan et al. Lemma 3. Assume algorithm SNRA has sorted accessed dj depth to Lj (j = 1, 2, . . , m), and the stopping rule has not been satisfied, algorithm SNRA will proceed to access the next object at least in one list. Proof. Let d be an array of [d1 , d2 , . . , dm ], in the rest of the paper when we say depth d , it means depth dj in Lj . We only need to prove that at depth d the best competitor has at least one missing field. Thus the remaining part of this lemma’s proof is the same as Lemma 1’s.

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