scalable skyline computation using object-based space partitioning
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
1
SCALABLE SKYLINE COMPUTATION USING OBJECT-BASED SPACE PARTITIONING
Shiming Zhang
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.