1 / 14

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

carson
Télécharger la présentation

Efficient Computation of Reverse Skyline Queries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related