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

More documents

Recommendations

Info

3.20 Computation of a Scaling Operation for a Raster Image . . . . . . . . 57 3.21 Slopes Along the X and Y Directions . . . . . . . . . . . . . . . . . . 58 3.22 Flow Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.23 Sobel Masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.24 Computation of an Edge-Detection for a Raster Image . . . . . . . . . 60 4.1 Types of Pre-Aggregates . . . . . . . . . . . . . . . . . . . . . . . . 66 4.2 Selected Queries for Pre-Aggregation (left) and Decomposed Queries (right) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.1 Sample Lattice Diagram for a Workload with Five Scaling Operations 79 5.2 Query Workload with Uniform Distribution . . . . . . . . . . . . . . 87 5.3 Query Workload with Poisson Distribution . . . . . . . . . . . . . . . 88 5.4 Selected Queries for Pre-Aggregation . . . . . . . . . . . . . . . . . 89 5.5 Query Workload with Peak Distribution . . . . . . . . . . . . . . . . 90 5.6 Selected Queries for Pre-Aggregation . . . . . . . . . . . . . . . . . 90 5.7 Query Workload with Step Distribution . . . . . . . . . . . . . . . . 91 5.8 Selected Queries for Pre-Aggregation . . . . . . . . . . . . . . . . . 92 5.9 Workload with Uniform Distribution along x, y, and t . . . . . . . . . 93 5.10 Average Query Cost over Storage Space . . . . . . . . . . . . . . . . 93 5.11 Selected Pre-Aggregates, c = 36% . . . . . . . . . . . . . . . . . . . 94 5.12 Workload with Uniform Distribution Along x, y, and Poisson distribution in t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.13 Average Query Cost as Space is Varied . . . . . . . . . . . . . . . . . 95 5.14 Selected Pre-Aggregates, c = 26% . . . . . . . . . . . . . . . . . . . 96 5.15 Workload with Poisson distribution Along x, y, and t . . . . . . . . . 96 5.16 Average Query Cost as Space is Varied . . . . . . . . . . . . . . . . . 97 5.17 Selected Pre-Aggregates, c = 30% . . . . . . . . . . . . . . . . . . . 97 5.18 Workload with Poisson Distribution Along x, y, and Uniform Distribution in t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.19 Average Query Cost as Space is Varied . . . . . . . . . . . . . . . . . 99 5.20 Selected Pre-Aggregates, c = 21% . . . . . . . . . . . . . . . . . . . 99
List of Tables 3.1 UNO and FAO Suitability Classifications . . . . . . . . . . . . . . . 43 3.2 Capability Indexes for Different Capability Classes . . . . . . . . . . 43 3.3 Array Algebra Classification of Geo-Raster Operations. . . . . . . . . 62 4.1 Cost Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2 Database and Queries of the Experiment. . . . . . . . . . . . . . . . . 74 4.3 Comparison of Query Evaluation Costs Using Pre-Aggregated Data and Original Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.1 Sample Pre-Aggregates. . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 ECHAM T-42 Climate Simulation Dimensions . . . . . . . . . . . . 100 5.3 4D Scaling: Scale Vector Distribution . . . . . . . . . . . . . . . . . 100 5.4 4D Scaling: Selected Pre-Aggregates . . . . . . . . . . . . . . . . . . 100 7
Page 1: Applying OLAP Pre-Aggregation Techn
Page 5 and 6: Acknowledgments I would like to exp
Page 7 and 8: Abstract Large multidimensional arr
Page 9 and 10: Contents 1 Introduction and Problem
Page 11: List of Figures 2.1 3D Array . . .
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
Page 59 and 60: 3.2 Geo-Raster Operations 53 is the
Page 61 and 62: 3.2 Geo-Raster Operations 55 3.2.4
Page 63 and 64:
3.2 Geo-Raster Operations 57 As in
Page 65 and 66:
3.2 Geo-Raster Operations 59 Local
Page 67 and 68:
3.3 Summary 61 Slicing The slicing
Page 69 and 70:
Chapter 4 Answering Basic Aggregate
Page 71 and 72:
4.1 Framework 65 pre-aggregated res
Page 73 and 74:
4.2 Cost Model 67 By partitioning t
Page 75 and 76:
4.2 Cost Model 69 Cost of independe
Page 77 and 78:
4.3 Implementation 71 Algorithm 1 Q
Page 79 and 80:
4.4 Experimental Results 73 Query E
Page 81 and 82:
4.5 Summary 75 pre-aggregates: inde
Page 83 and 84:
Chapter 5 Pre-Aggregation Support B
Page 85 and 86:
5.2 Conceptual Framework 79 Figure
Page 87 and 88:
5.2 Conceptual Framework 81 Benefit
Page 89 and 90:
5.4 Answering Scaling Operations Us
Page 91 and 92:
5.5 Experimental Results 85 Algorit
Page 93 and 94:
5.5 Experimental Results 87 (a) Que
Page 95 and 96:
5.5 Experimental Results 89 (a) Sel
Page 97 and 98:
5.5 Experimental Results 91 vectors
Page 99 and 100:
5.5 Experimental Results 93 root no
Page 101 and 102:
5.5 Experimental Results 95 Figure
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
5.6 Summary 101 we considered non-u
Page 109 and 110:
Chapter 6 Conclusion One of the big
Page 111 and 112:
6.1 Future Work 105 more non-spatio
Page 113 and 114:
Bibliography [1] Blakeley J. A., La
Page 115 and 116:
BIBLIOGRAPHY 109 [22] Moon B., Vega
Page 117 and 118:
BIBLIOGRAPHY 111 [47] ESRI Inc. Arc
Page 119 and 120:
BIBLIOGRAPHY 113 [73] Stefanovic N.
Page 121:
BIBLIOGRAPHY 115 [97] Kotidis Y. an
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?