Database Systems for Advanced Applications: 20th by Matthias Renz, Cyrus Shahabi, Xiaofang Zhou, Muhammad Aamir

By Matthias Renz, Cyrus Shahabi, Xiaofang Zhou, Muhammad Aamir Cheema

This quantity set LNCS 9049 and LNCS 9050 constitutes the refereed lawsuits of the twentieth foreign convention on Database structures for complicated functions, DASFAA 2015, held in Hanoi, Vietnam, in April 2015. The sixty three complete papers provided have been rigorously reviewed and chosen from a complete of 287 submissions. The papers hide the next subject matters: info mining; facts streams and time sequence; database garage and index; spatio-temporal info; glossy computing platform; social networks; details integration and knowledge caliber; info retrieval and summarization; safety and privateness; outlier and imbalanced information research; probabilistic and unsure information; question processing.

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Extra resources for Database Systems for Advanced Applications: 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015, Proceedings, Part I

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If |LR1 ∩ LR2 | is less than minsup, then Q is infrequent and should be excluded from further processing (lines 2-3 in Algorithm IsFrequent of Fig. 3). As verified by our experimental results, substantial CPU cost can be saved using this early-detection of infrequent candidate patterns. Representing Occurrence Lists as Bitmaps. The occurrence list LX of a pattern node X labeled by a on T can be represented by a bitmap on La that has a ‘1’ bit at position i iff LX comprises the tree node at position i of La .

Algorithm T wigStack joins multiple inverted lists at a time to avoid generating intermediate join results. It uses a stack for every tree pattern node, and works in two phases. In the first phase, it computes occurrences of the individual root-to-leaf paths of the pattern. In the second phase, it merge-joins the path occurrences to compute the results for the pattern (Function MergeAllPathOccurrences in Fig. 3). An important property of T wigStack is that whenever a data node x is pushed into the stack of a pattern node X having child nodes Y1 , .

Further, if X is the image of nodes X1 and X2 defined by the homomorphisms from the left and right parent of Q, respectively, we can compute X using the intersection, LX1 ∩ LX2 , of LX1 and LX2 which is the sublist of LX1 and LX2 comprising the nodes that appear in both LX1 and LX2 (line 4 in Algorithm IsFrequent of Fig. 3). Using Proposition 1, we can compute Q using only the occurrence list sets of its parents. Thus, we only need to store with each frequent pattern its occurrence list set. Our method is space efficient since the occurrence lists can encode in linear space an exponential number of occurrences for the pattern [3].

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