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

By Gio (Editor) Wiederhold

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ACM Transactions on Database Systems, Vol. 30, No. 1, March 2005. Progressive Skyline Computation in Database Systems • 43 algorithms have been proposed for the case that the whole dataset fits in memory [Preparata and Shamos 1985]. Top-K (or ranked) queries retrieve the best K objects that minimize a specific preference function. As an example, given the preference function f (x, y) = x + y, the top-3 query, for the dataset in Figure 1, retrieves < i, 5 >, < h, 7 >, < m, 8 > (in this order), where the number with each point indicates its score.

Its children (e3 , e4 , e5 ). The next expanded entry is again the one with the minimum mindist (e3 ), in which the first nearest neighbor (i) is found. This point (i) belongs to the skyline, and is inserted to the list S of skyline points. Notice that up to this step BBS behaves like the best-first nearest-neighbor algorithm of Hjaltason and Samet [1999]. The next entry to be expanded is e6 .

Each of the remaining subdivisions, however, will be searched by two queries, for example, a skyline point in subdivision 2 will be discovered by both the second and third queries. In general, for d > 2, the overlapping of the partitions necessitates duplicate elimination. Kossmann et al. [2002] proposed the following elimination methods: —Laisser-faire: A main memory hash table stores the skyline points found so far. When a point p is discovered, it is probed and, if it already exists in the hash table, p is discarded; otherwise, p is inserted into the hash table.

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