Compressing Relations And Indexes

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Compressing Relations And Indexes. Jonathan GoldsteinRaghu Ramakrishnan Uri Shaft Department of Compter Sciences, University of Wisconsin-Madison June 18, 1997. Agenda. Introduction Compressing A Relation Compression Applied to Rectangle Base Indexes Performance Evaluation - PowerPoint PPT Presentation

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  • Compressing Relations And IndexesJonathan GoldsteinRaghu RamakrishnanUri Shaft

    Department of Compter Sciences, University of Wisconsin-Madison

    June 18, 1997

  • AgendaIntroductionCompressing A RelationCompression Applied to Rectangle Base IndexesPerformance EvaluationQuestions and Remarks

  • IntroductionPage level CompressionPerformance StudyApplication to B-trees and R-treesMultidimensional bulk loading algorithm

  • Introduction

  • Introduction

  • Compressing A relationFrames Of ReferenceNon numeric attributesFile level compression

  • Frames of Reference

  • Lossy CompressionPoint approximation in lossy compression

  • Compressing an indexing structureCompressing a B-treeCompressing a rectangle based indexing structureCompression oriented Bulk Loading

  • Rectangle Based indexing qualities

  • Changing the frame of reference

  • Bulk-Loading AlgorithmInput. A set of points in some n-dimentional space.Output. A partition of the inut into subsets.Requirements. The partition shuold group points that are close to each other in the same group as much as possiblg

  • GB-Pack compression oriented bulk loading

  • GB-Pack compression oriented bulk loadingQualities:trading off some tree quality for increased compression.number of entries per page is data-dependent.cutting a dimension in a value boundary in the data.

  • GB-Pack compression oriented bulk loading

  • GB-Pack compression oriented bulk loading

  • GB-Pack compression oriented bulk loading

  • Performance EvaluationRelational Compression Experiments.CPU vs. I/O Costs.Comparison With Techniques in commercial systems.Importance of Tuple-Level Decompression.R-tree Compression Experiments.

  • Synthetic Data SetsSize: The number of tuples in the relation.Dimensionality: The number of attributes of the relations.Range: The range of values for the attributes.Distribution :uniform(worst case) / exponential.Partition Strategy.Page size.

  • Sales Data SetSales data set. Compression Achieved versus dimensionality

  • CPU vs. I/O Costs

  • R-tree Compression ExperimentsTesting the quality of R-trees on Sales Data Set.

  • Questions And Remarks

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