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Efficient Computation of Reverse Skyline Queries

Efficient Computation of Reverse Skyline Queries. VLDB 2007. Outline. Introduction Dynamic Skyline Query Branch-and-Bound for Reversed Skylines Reversed Skylines with Approximations Experimental Results Conclusion. Skyline. Important new class of queries

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Efficient Computation of Reverse Skyline Queries

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  1. Efficient Computation of Reverse Skyline Queries VLDB 2007

  2. Outline • Introduction • Dynamic Skyline Query • Branch-and-Bound for Reversed Skylines • Reversed Skylines with Approximations • Experimental Results • Conclusion

  3. Skyline • Important new class of queries • Given: a set of d-dimensional points • Result:points that are not dominated by others • x dominates y x is as good as y in all dimensions and better in at least on dimension • Exanple(collection of used cars)

  4. Dynamic Skyline Query • Motivation(customer perspective) • Ideal used car:120 hp, 30000 km, build 2005, … • Find all cars that are close to customer’s specification • Skyline query relative to a reference point ref • x dominates y iff x is not farer from ref than y in all dimensions and in at least one dimension closer to ref • Example(Used Car Database)

  5. Dynamic Skyline Query • Without loss of generality • Example(Used Car Database)

  6. Reverse Skyline Query • Motivation (dealer perspective) • Given: the preferences of customer, the collection of used cars • Does it make sense to offer a car X to one of my customers? Car X is interesting, if it is in the skyline of a preference

  7. Reverse Skyline Query • Reverse Skyline query of q • RSL(q) = points whose dynamic skyline contains q • Two Algorithms • Assumption: R-tree on set P • Branch-and-bound algorithm (BBRS) • Reversed Skyline Search with Approximation(RSSA)

  8. BBRS: Branch-and-Bound algorithm • Assumption • Multidimensional index (e.g.R-tree) on point set P • Goal • Processing reversed skyline of point q without transformation • Global Skyline GSL(q) • Points that are not globally dominated

  9. Important Properties • RSL(q) GSL(q)

  10. Reverse Skyline with Approximations • Important property • If any from DSL(p) dominates q p is not in RSL(q)

  11. Approximations • For each p we keep a subset DSL(p) of constant size • Parameter k • Filter Step • If q dominates one of the samples p is in RSL(q) • If a sample dominates q p is not in RSL(q) • Otherwise, call the refinement step

  12. Cont.

  13. Comparsion RSSA vs BBRS • Performance as a function of dimensionality

  14. Conclusion • Reverse Skyline are important for finding interesting points • Dealer perspective: What kind of items are interesting to my customers? • Two Algorithms • BBRS • RSSA • Future Work • Accurate Approximation of skylines for d >2

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