Download ACM Transactions on Database Systems (March) by Gio (Editor) Wiederhold PDF

By Gio (Editor) Wiederhold

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Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing. 3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Experimentation Additional Key Words and Phrases: Skyline query, branch-and-bound algorithms, multidimensional access methods This research was supported by the grants HKUST 6180/03E and CityU 1163/04E from Hong Kong RGC and Se 553/3-1 from DFG. Authors’ addresses: D.

Finally, Section 7 concludes the article and describes directions for future work. 2. RELATED WORK This section surveys existing secondary-memory algorithms for computing skylines, namely: (1) divide-and-conquer, (2) block nested loop, (3) sort first skyline, (4) bitmap, (5) index, and (6) nearest neighbor. Specifically, (1) and (2) were proposed in Borzsonyi et al. [2001], (3) in Chomicki et al. [2003], (4) and (5) in Tan et al. [2001], and (6) in Kossmann et al. [2002]. We do not consider the sorted list scan, and the B-tree algorithms of Borzsonyi et al.

Partitions. , i > log N ) so that all partitions in the to-do list are empty. Assuming that the empty queries are performed at the end, the size of the to-do list can be obtained by summing the number e of empty queries at each recursion level i: log N 2i−1 = N − 1. i=1 The implication of the above equation is that, even in 3D, NN may behave like a main-memory algorithm (since the to-do list, which resides in memory, is the same order of size as the input dataset). Using the same reasoning, for arbitrary dimensionality d > 2, e = ((d −1)log N ), that is, the to-do list may become orders of magnitude larger than the dataset, which seriously limits the applicability of NN.

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