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

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62 3. Fundamental Geo-Raster Operations Table 3.3. Array Algebra Classification of Geo-Raster Operations. Operation MARRAY COND SORT AFFINE 1. Count x 2. Add x 3. Average x 4. Maximum x 5. Minimum x 6. Majority x x 7. Minority x x 8. Std. Deviation x 9. Median x x 10. Variance x 11. Top-k x 12. Histogram x x 13. Diversity x x 14. Proximity x 15. Arithmetic x 16. Trigonometric x 17. Boolean x 18. Logical x 19. Overlay x 20. Re-classification x 21. Translation x 22. Rotation x 23. Scaling x x x 24. Slicing x 25. Edge Detection x x 26. Slope/Aspect x 27. Local drain directions (ldd) x x
Chapter 4 Answering Basic Aggregate Queries Using Pre-Aggregated Data As discussed in previous chapters, aggregation is an important mechanism that allows users to extract general characterizations from very large repositories of data. In this chapter, we study the effect of selecting a set of aggregate queries, compute their results and use them for subsequent query requests. In particular, we study the effect of pre-aggregation in computing aggregate queries in the field of GIS and remotesensing imaging applications. We introduce a pre-aggregation framework that distinguishes among different types of pre-aggregates for computing a query. We show that in most cases, several preaggregates may qualify for answering an aggregate query and address the problem of selecting the best pre-aggregate in terms of execution time. To this end, we introduce a model that measures the cost of using qualified pre-aggregates for the computation of a query. We then present an algorithm that selects the best pre-aggregate for computing a query. We measure the performance of our algorithms in an array database management system (RasDaMan), and show that our algorithms give much better performance over straightforward methods. 4.1 Framework Most major database management systems allow the user to store query results through a process known as view materialization. The query optimizer may then automatically use the materialized data to speed up the evaluation of a new query. Queries that benefit from using materialized data are those that involve the summarization of large amounts of data. They are known as aggregate queries because their query statements include one or more aggregate functions. The ANSI SQL:2008 standard defines a wide variety of aggregate functions including: COUNT, SUM, AVG, MAX, MIN, EVERY, ANY, SOME, VAR POP, VAR SAMP, STDDEV POP, STDDEV SAMP, AR- RAY AGG, REGR COUNT, COVAR POP, COVAR SAMP, CORR, REGR R2, REGR SLOPE, and REGR INTER-CEPT [20]. 63
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Applying OLAP Pre-Aggregation Techn
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Acknowledgments I would like to exp
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Abstract Large multidimensional arr
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Contents 1 Introduction and Problem
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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
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: 3.3 Summary 61 Slicing The slicing
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 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
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BIBLIOGRAPHY 113 [73] Stefanovic N.
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BIBLIOGRAPHY 115 [97] Kotidis Y. an
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