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

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46 3. Fundamental Geo-Raster Operations (a) Grey 8-bit raster A (b) Grey 8-bit raster B (c) Output raster Figure 3.7. Computation of an Overlay Operation Considering Values Greater than Zero and max. SQL:1999 adds every, some and any. OLAP functions were first published as an addendum to the ISO SQL:1999 standard. They have since been completely incorporated into both SQL:2003 and recently published SQL:2008 ISO SQL Standards. OLAP functions include rank, ntile, cume dist, percent rank, row number, percentile cont, and percentile disc. Add The add operation sums up the content of the cells and returns the total as a scalar value. It can be applied in two or more rasters with an identical spatial domain, returning a new raster with the same spatial domain. In this case, the cells of the new raster contain the sum of the inputs computed on a cell-by-cell basis. As an example of the add operation in a single raster consider the following query: Query 3.2.8. Return the sum of all cell values of the raster shown in Fig. 3.8(a). Results are shown in Fig. 3.8. add cells(A) = COND +,sdom(A),i (A[i])
3.2 Geo-Raster Operations 47 (a) Original NRG raster (b) Output result Figure 3.8. Calculation of the Total Sum of Cell Values in a Raster Count The count operation returns the number of cells that fulfill a boolean condition applied to a raster. For example, consider the following query: Query 3.2.9. Return the number of cells of raster A of boolean type, containing true value in the green channel. Average count cells(A) = COND +,sdom(A),i (A[i].green = 1) The average operation returns a scalar value representing the mean of all values contained in a raster. As an example consider the following query: Query 3.2.10. Return the average of the cell values in each channel of the NRG image shown in Fig. 3.9(a). Let sum cells(A) be a function calculated as shown in Section 3.2.2, and card(sdom(A)) a function returning the cardinality of A. Then, the average of A is calculated as follows: sum cells(A) avg cells(A) = card(sdom(A)) Results are shown in Fig. 3.9. Maximum A maximum operation returns the largest cell value contained in a raster of numerical type. As an example, consider the following query:
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 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: 3.2 Geo-Raster Operations 45 turn i
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
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5.5 Experimental Results 97 Figure
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5.5 Experimental Results 99 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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Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

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