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By Jiajun Liu, Zi Huang, Heng Tao Shen, Xiaofang Zhou (auth.), Jeffrey Xu Yu, Myoung Ho Kim, Rainer Unland (eds.)

This quantity set LNCS 6587 and LNCS 6588 constitutes the refereed complaints of the sixteenth foreign convention on Database structures for complex purposes, DASFAA 2011, held in Saarbrücken, Germany, in April 2010.
The fifty three revised complete papers and 12 revised brief papers provided including 2 invited keynote papers, 22 demonstration papers, four business papers, eight demo papers, and the summary of one panel dialogue, have been conscientiously reviewed and chosen from a complete of 225 submissions. the themes coated are social community, social community and privateness, information mining, chance and uncertainty, move processing, graph, XML, XML and graph, similarity, looking and electronic protection, spatial queries, question processing, in addition to indexing and excessive performance.

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Read or Download Database Systems for Advanced Applications: 16th International Conference, DASFAA 2011, Hong Kong, China, April 22-25, 2011, Proceedings, Part II PDF

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Extra resources for Database Systems for Advanced Applications: 16th International Conference, DASFAA 2011, Hong Kong, China, April 22-25, 2011, Proceedings, Part II

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But they only consider relationships between individual features and each solution. This leads to trouble, since they ignore complex relationships between multiple features and each solution. For example, two features may be individually correlated with a certain solution, but together they may not, or vice versa. In contrast, USIMSCAR exploits not only the individual relationships, but complex relationships between multiple features (itemsets in scars) and solutions. Several researchers have attempted to augment SBR with certain factors obtained through statistical learning and adaptation knowledge.

Since only expanded states are indexed in the data structure, the matched state qi has to be transformed back to the original state qi , according to Equation 2. WeightedStateVoting() is the method to rank all the candidates (line 8). When the histogram intersection is applied, the ranking score for each candidate is computed based on q¯i , where q¯i is the value of matched state between Q and a candidate. Top-k candidates are returned for the actual histogram intersection computations to find the nearest neighbor to Q (line 9).

WeightedStateVoting() is the method to rank all the candidates (line 8). When the histogram intersection is applied, the ranking score for each candidate is computed based on q¯i , where q¯i is the value of matched state between Q and a candidate. Top-k candidates are returned for the actual histogram intersection computations to find the nearest neighbor to Q (line 9). 2 = {img4 } without state expansion. The weighted state-voting result is for img1 , img2 and img3 is 1, 1+3, and 3+2 respectively.

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