1 / 20

Scalable Skyline Computation Using Object-based Space Partitioning

Scalable Skyline Computation Using Object-based Space Partitioning. Shiming Zhang Nikos Mamoulis David W. Cheung sigmod 2009. Outline. Introduction Object-based Space Partitioning Recursive Object-based Space Partitioning Left-Child/Right-Sibling Skyline Tree OSPSOnSortingFirst

vila
Download Presentation

Scalable Skyline Computation Using Object-based Space Partitioning

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. Scalable Skyline Computation Using Object-based Space Partitioning ShimingZhang Nikos Mamoulis David W. Cheung sigmod 2009

  2. Outline • Introduction • Object-based Space Partitioning • Recursive Object-based Space Partitioning • Left-Child/Right-Sibling Skyline Tree • OSPSOnSortingFirst • OSPSOnPartitioningFirst • FilterDominatedPartitions • Experimental Results • Conclusions

  3. Introduction(1) • Skyline queries are useful in multi-criteria decision making applications that involve high dimensional and large datasets. • There is a number of methods that operate on pre-computed indexes on the data. • Compare each accessed point with the skyline points found so far.

  4. Introduction(2) • Object-based space partitioning(OSP) scheme, which recursively divides the d-dimensional space into separate partitions w.r.t. a reference skyline object. • Organizes the current skyline points in a search tree. • Object o dominates another object o' iff o is not worse than o' in all dimensions and better than o' in at least one dimension.

  5. Notation

  6. Object-based Space Partitioning reference skyline

  7. Recursive Object-based Space Partitioning reference skyline

  8. Why can safely skip? • Skip all incomparable partitions according to Corollary 1

  9. Left-Child/Right-Sibling Skyline Tree

  10. Left-Child/Right-Sibling Skyline Tree

  11. LCRS Tree Growth

  12. Trace

  13. OSP Skyline Algorithms 1

  14. OSP Skyline Algorithms 2

  15. OSP Skyline Algorithms

  16. OSP Skyline Algorithms

  17. Experimental Evaluation • Three types of synthetic datasets • anti-correlated (AC) • NBA • uniform and independent (UI) • Household • correlated (CO) • Color

  18. Experimental Results

  19. Experimental Results

  20. Conclusions • Proposed an efficient set of skyline evaluation algorithms that are based on the idea of organizing the discovered skyline points in a tree. • Each candidate skyline object only needs to be compared for dominance with a small subset of the existing skyline points. (skip incomparable sets ) • Makes our solutions scalable to the dimensionality, a feature that all previously proposed skyline algorithms lack.

More Related