Sampling
DepartmentofComputerScience
UniversityofCaliforniaSantaBarbara,CA93106
ChristianA.Lang
DepartmentofComputerScience
UniversityofCaliforniaSantaBarbara,CA93106
AmbujK.Singh
clang@cs.ucsb.edu
ABSTRACT
ambuj@cs.ucsb.edu
1.INTRODUCTION
2.RELATEDWORK
2.1UniformDataModels
2.2GloballyParametricModels
2.3LocallyParametricModels
2.4Sampling-basedModels
3.SAMPLING-BASED
PREDICTIONMODEL
3.1TheBasicModel
Sample data
(a)
(c)
Build index
Build index
(b)
Grow pages
(d)
Original page layoutSample page layout
3.2CalculationoftheCompensationFactor
hlowerhupperUpper tree
...
Lower trees
4.2PredictinginPhases
(a) Upper tree leaf pages before growingFirst sampleSecond sample
(b) Upper tree leaf pages after growing
N points(a) loadingM points. . .(b) distributingk consecutive disk areasoriginal data filemain memoryM/k points (average)4.5.3Effectof
onI/OCost
4.6I/OCostofthePredictions
4.5.2
4.7OtherIndexStructures
5.EXPERIMENTALRESULTS
5.1RuntimeofthePredictions
5.2AccuracyofthePredictions
3000
2500
2000
noitcid1500
erp1000
500
0
05001000
1500
200025003000
on-disk
3000
2500
2000
noitcid1500
erp1000
500
0
050010001500200025003000
on-disk
5.3ComparisonwithOtherModels
1.2
PredictionMeasurement
1
0.8
)s ni( tso0.6
c O/I0.4
0.2
0
40961638432768
65536
131072
Page size (in bytes)
600
PredictionMeasurement500
400
sessecca300
egaP200
100
0
0102030
405060
Index dimensionality
6.2Determiningality
theOptimalDataDimension-7.CONCLUSIONS
8.ACKNOWLEDGEMENTS
9.REFERENCES
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