Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

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This allows us to identify those operations that require data summarization and that therefore may benefit from pre-aggregation. We introduce a conceptual framework and cost model for rewriting basic aggregate queries in terms of pre-aggregated data, and conduct experiments to assess the performance of our algorithms. Results show that query response times can be substantially reduced by strategically selecting the pre-aggregate with the least cost in terms of execution time. We also investigate the problem of selecting a set of queries for pre-aggregation, but failed to find an analytical solution for all possible types of aggregate queries. Nevertheless, we present a framework and algorithms for the selection of scaling operations for pre-aggregation considering 2D, 3D, and 4D datasets. The results of our experiments with 2D datasets outperform the results of image pyramids, the current technique used to speed up scaling operations on 2D datasets. Furthermore, our experiments on 3D and 4D datasets show that query response types can also be substantially reduced by intelligently selecting a set of scaling operations for pre-aggregation. The work presented in this thesis is the first of its kind for array databases in scientific applications.
Contents 1 Introduction and Problem Statement 9 1.1 Overview of Thesis and Contributions . . . . . . . . . . . . . . . . . 12 1.2 Publications Related to this Thesis . . . . . . . . . . . . . . . . . . . 12 2 Background and Related Work 15 2.1 Array Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.1.1 Basic Notion of Arrays . . . . . . . . . . . . . . . . . . . . . 15 2.1.2 2D Data Models . . . . . . . . . . . . . . . . . . . . . . . . 16 2.1.3 Multidimensional Data Models . . . . . . . . . . . . . . . . 17 2.1.4 Storage Management . . . . . . . . . . . . . . . . . . . . . . 18 2.1.5 2D Pre-Aggregation . . . . . . . . . . . . . . . . . . . . . . 19 2.1.6 Pre-Aggregation Beyond 2D . . . . . . . . . . . . . . . . . . 23 2.1.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 On-Line Analytical Processing (OLAP) . . . . . . . . . . . . . . . . 25 2.2.1 OLAP Data model . . . . . . . . . . . . . . . . . . . . . . . 25 2.2.2 OLAP Operations . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.3 OLAP Architectures . . . . . . . . . . . . . . . . . . . . . . 26 2.2.4 OLAP Pre-Aggregation . . . . . . . . . . . . . . . . . . . . 30 2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 Fundamental Geo-Raster Operations 37 3.1 Array Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.1.1 Constructor . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.1.2 Condenser . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.1.3 Sorter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Geo-Raster Operations . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.1 Mathematical Operations . . . . . . . . . . . . . . . . . . . . 39 3.2.2 Aggregation Operations . . . . . . . . . . . . . . . . . . . . 45 3.2.3 Statistical Aggregate Operations . . . . . . . . . . . . . . . . 51 3.2.4 Affine Transformations . . . . . . . . . . . . . . . . . . . . . 55 3.2.5 Terrain Analysis . . . . . . . . . . . . . . . . . . . . . . . . 57 3.2.6 Other Operations . . . . . . . . . . . . . . . . . . . . . . . . 59 3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3
Page 1: Applying OLAP Pre-Aggregation Techn
Page 5 and 6: Acknowledgments I would like to exp
Page 7: Abstract Large multidimensional arr
Page 11 and 12: List of Figures 2.1 3D Array . . .
Page 13 and 14: List of Tables 3.1 UNO and FAO Suit
Page 15 and 16: Chapter 1 Introduction and Problem
Page 17 and 18: Relevant and complementary question
Page 19 and 20: 1.2 Publications Related to this Th
Page 21 and 22: Chapter 2 Background and Related Wo
Page 23 and 24: 2.1 Array Databases 17 Figure 2.2 s
Page 25 and 26: 2.1 Array Databases 19 toward the s
Page 27 and 28: 2.1 Array Databases 21 • Bilinear
Page 29 and 30: 2.1 Array Databases 23 given image
Page 31 and 32: 2.2 On-Line Analytical Processing (
Page 39 and 40: 2.3 Discussion 33 spatial-vector da
Page 41 and 42: 2.3 Discussion 35 • Both applicat
Page 43 and 44: Chapter 3 Fundamental Geo-Raster Op
Page 45 and 46: 3.2 Geo-Raster Operations 39 3.1.2
Page 47 and 48: 3.2 Geo-Raster Operations 41 multip
Page 49 and 50: 3.2 Geo-Raster Operations 43 Table
Page 51 and 52: 3.2 Geo-Raster Operations 45 turn i
Page 53 and 54: 3.2 Geo-Raster Operations 47 (a) Or
Page 55 and 56: 3.2 Geo-Raster Operations 49 Query
Page 57 and 58: 3.2 Geo-Raster Operations 51 contai
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3.2 Geo-Raster Operations 53 is the
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3.2 Geo-Raster Operations 55 3.2.4
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3.2 Geo-Raster Operations 57 As in
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3.2 Geo-Raster Operations 59 Local
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3.3 Summary 61 Slicing The slicing
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Chapter 4 Answering Basic Aggregate
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4.1 Framework 65 pre-aggregated res
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4.2 Cost Model 67 By partitioning t
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4.2 Cost Model 69 Cost of independe
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4.3 Implementation 71 Algorithm 1 Q
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4.4 Experimental Results 73 Query E
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4.5 Summary 75 pre-aggregates: inde
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Chapter 5 Pre-Aggregation Support B
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5.2 Conceptual Framework 79 Figure
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5.2 Conceptual Framework 81 Benefit
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5.4 Answering Scaling Operations Us
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5.5 Experimental Results 85 Algorit
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5.5 Experimental Results 87 (a) Que
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5.5 Experimental Results 89 (a) Sel
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5.5 Experimental Results 91 vectors
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5.5 Experimental Results 93 root no
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5.5 Experimental Results 95 Figure
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5.6 Summary 101 we considered non-u
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Chapter 6 Conclusion One of the big
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6.1 Future Work 105 more non-spatio
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Bibliography [1] Blakeley J. A., La
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BIBLIOGRAPHY 109 [22] Moon B., Vega
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BIBLIOGRAPHY 111 [47] ESRI Inc. Arc
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BIBLIOGRAPHY 113 [73] Stefanovic N.
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BIBLIOGRAPHY 115 [97] Kotidis Y. an
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