Database Systems for Advanced Applications: 17th - download pdf or read online

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

ISBN-10: 3642290345

ISBN-13: 9783642290343

ISBN-10: 3642290353

ISBN-13: 9783642290350

This quantity set LNCS 7238 and LNCS 7239 constitutes the refereed complaints of the seventeenth overseas 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 conscientiously reviewed and chosen from a complete of 159 submissions. the subjects lined are question processing and optimization, facts semantics, XML and semi-structured info, facts mining and information discovery, privateness and anonymity, info administration within the internet, graphs and information mining purposes, temporal and spatial info, top-k and skyline question processing, details retrieval and suggestion, indexing and seek structures, cloud computing and scalability, memory-based question processing, semantic and selection aid platforms, social information, info mining.

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Read or Download Database Systems for Advanced Applications: 17th International Conference, DASFAA 2012, Busan, South Korea, April 15-19, 2012, Proceedings, Part II PDF

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Additional resources for Database Systems for Advanced Applications: 17th International Conference, DASFAA 2012, Busan, South Korea, April 15-19, 2012, Proceedings, Part II

Example text

Tuple x dynamic dominates y, if x doesn’t have longer distance to q than y for each dimension i, and x has shorter distance to q than y for at least one dimension j. t. j|). t. q. t. q of P contains all the points whose dynamic skyline contains q. t. q. Reverse skyline plays an important role in market decision-making. With the development of economic and the progress of technological means, the ﬂow property and uncertainty of data become more and more apparent. So reverse skyline query can be introduced to uncertain data streams which is very useful for many applications.

The proposed algorithm is executed on this IIP probabilistic data for the top-10 best probability query, the answer to this query being (t1 , t2 , t3 , t4 , t5 , t6 , t7 , t8 , t9 , t10 , t11 ) as shown in Table 7. Tuple t1 has the highest value in the Drifted Days attribute, t2 is the other tuple which has the same value or the second highest value in the Drifted Days attribute, and so on. The answer to the top-10 best probability query contains the 10 highest scoring tuples in the data set (t1 , t2 , t3 , t4 , t5 , t6 , t7 , t8 , t9 , t10 ).

7 (b) Window Size Vs. Number Fig. 10. The Inﬂuence of Threshold Through the experiments above, we can conclude that no matter what kind of data distribution, average response time of OPRS become longer and max reserved tuples’ number become more with the increase of data dimensionality and sliding window size, while the response time become shorter and max reserved tuples’ number become fewer with the increase of the threshold. The experimental results accord with the actual situation. Probabilistic Reverse Skyline Query Processing over Uncertain Data Stream 6 31 Conclusions Many applications can be modeled by uncertain data stream, reverse skyline plays an important role in market decision-making, environmental monitoring and market analysis.

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