Download Database Systems for Advanced Applications: 17th by Trieu Minh Nhut Le, Jinli Cao (auth.), Sang-goo Lee, Zhiyong PDF

By Trieu Minh Nhut Le, Jinli Cao (auth.), Sang-goo Lee, Zhiyong Peng, Xiaofang Zhou, Yang-Sae Moon, Rainer Unland, Jaesoo Yoo (eds.)

This quantity set LNCS 7238 and LNCS 7239 constitutes the refereed lawsuits of the seventeenth foreign convention on Database structures for complicated functions, DASFAA 2012, held in Busan, South Korea, in April 2012.
The forty four revised complete papers and eight brief papers offered including 2 invited keynote papers, eight business papers, eight demo displays, four tutorials and 1 panel paper have been rigorously reviewed and chosen from a complete of 159 submissions. the themes coated are question processing and optimization, facts semantics, XML and semi-structured info, info mining and information discovery, privateness and anonymity, facts administration within the internet, graphs and information mining functions, temporal and spatial info, top-k and skyline question processing, details retrieval and advice, indexing and seek structures, cloud computing and scalability, memory-based question processing, semantic and determination help platforms, social facts, information mining.

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Figure 9(a) records the average response time of each tuple in OPRS for two data distributions. Figure 9(b) records the max reserved tuples’ number, c-max represents the max reserved tuples’ number in cluster distribution data set, while u-max represents that in uniform distribution data set. cluster uniform 18 16 14 12 100 200 300 400 500 8 c-max u-max 7 6 5 100 200 Siling Window Size(× 103) 300 400 500 Siling Window Size(× 103) (a) Window Size Vs. Time (b) Window Size Vs. Number Fig. 9. The Influence of Sliding Window Size 140 Response Time/ms 120 Reserved Tuples Number(× 103) Finally, we discuss the influence of threshold t.

As a results, the k-best ranking scores property of U-top-k query answer is fail (Fail). E-rank [3]: A new expected score is produced by the expected ranking to select the top-k ranking query for probabilistic data. The authors used the ranking to calculate the new expected score for their proposal to remove the magnitude of normal expected score limitations. The magnitude of the normal expected score is a tuple having low top-k probability and a high score, giving it the highest expected score.

In a nutshell the problem is the intuitive interleaving of each individual user’s attribute value preferences with the generally applicable preferences on attribute semantics as specified in the query. Whereas skyline queries up to now only dealt with relaxing value preferences, the new additional relaxation in attribute semantics is owed to the linked open data. Let’s extend our example from above: Example: A user might be interested in famous Nobel laureates in physics that were born in Munich, Germany.

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