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SCALABLE SKYLINE COMPUTATION USING OBJECT-BASED SPACE PARTITIONING

Shiming Zhang

Nikos Mamoulis

David W. Cheung

sigmod 2009

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OUTLINE

Introduction Object-based Space Partitioning Recursive Object-based Space Partitioning Left-Child/Right-Sibling Skyline Tree

OSPSOnSortingFirst OSPSOnPartitioningFirst

FilterDominatedPartitions

Experimental Results Conclusions

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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.

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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.

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NOTATION

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OBJECT-BASED SPACE PARTITIONING

reference skyline

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RECURSIVE OBJECT-BASED SPACE PARTITIONING

reference skyline

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WHY CAN SAFELY SKIP?

Skip all incomparable partitions according to Corollary 1

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LEFT-CHILD/RIGHT-SIBLING SKYLINE TREE

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LEFT-CHILD/RIGHT-SIBLING SKYLINE TREE

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LCRS TREE GROWTH

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TRACE

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OSP SKYLINE ALGORITHMS 1

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OSP SKYLINE ALGORITHMS 2

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OSP SKYLINE ALGORITHMS

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OSP SKYLINE ALGORITHMS

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EXPERIMENTAL EVALUATION

Three types of synthetic datasets anti-correlated (AC)

NBA uniform and independent (UI)

Household correlated (CO)

Color

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EXPERIMENTAL RESULTS

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EXPERIMENTAL RESULTS

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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.