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


<ul><li><p>Compressing Relations And IndexesJonathan GoldsteinRaghu RamakrishnanUri Shaft</p><p>Department of Compter Sciences, University of Wisconsin-Madison</p><p>June 18, 1997</p></li><li><p>AgendaIntroductionCompressing A RelationCompression Applied to Rectangle Base IndexesPerformance EvaluationQuestions and Remarks</p></li><li><p>IntroductionPage level CompressionPerformance StudyApplication to B-trees and R-treesMultidimensional bulk loading algorithm</p></li><li><p>Introduction</p></li><li><p>Introduction</p></li><li><p>Compressing A relationFrames Of ReferenceNon numeric attributesFile level compression</p></li><li><p>Frames of Reference</p></li><li><p>Lossy CompressionPoint approximation in lossy compression</p></li><li><p>Compressing an indexing structureCompressing a B-treeCompressing a rectangle based indexing structureCompression oriented Bulk Loading</p></li><li><p>Rectangle Based indexing qualities</p></li><li><p>Changing the frame of reference</p></li><li><p>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</p></li><li><p>GB-Pack compression oriented bulk loading</p></li><li><p>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.</p></li><li><p>GB-Pack compression oriented bulk loading</p></li><li><p>GB-Pack compression oriented bulk loading</p></li><li><p>GB-Pack compression oriented bulk loading</p></li><li><p>Performance EvaluationRelational Compression Experiments.CPU vs. I/O Costs.Comparison With Techniques in commercial systems.Importance of Tuple-Level Decompression.R-tree Compression Experiments.</p></li><li><p>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.</p></li><li><p>Sales Data SetSales data set. Compression Achieved versus dimensionality </p></li><li><p>CPU vs. I/O Costs</p></li><li><p>R-tree Compression ExperimentsTesting the quality of R-trees on Sales Data Set.</p></li><li><p>Questions And Remarks</p></li></ul>