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

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74 4. Answering Basic Aggregate Queries Using Pre-Aggregated Data Table 4.2. Database and Queries of the Experiment. Qid Description Q1 select add cells(y[6000:10000, 29000:32000]) from blacksea as y were oid(y) = 49153 Q2 select add cells(y[7000:10000, 29000:31000]) from blacksea as y where oid(y) = 49154 Q3 select add cells(y[6700:10000, 28000:30000]) from blacksea as y where oid(y) = 49155 Q4 select add cells(y[7680:8191, 29000:31000]) from blacksea as y where oid(y) = 49153 Q5 select add cells(y[8704:9215, 29000:31000]) from blacksea as y where oid(y) = 49154 Q6 select add cells(y[9728:10000, 29000:31000]) from blacksea as y where oid(y) = 49155 Q7 select add cells(y[7680:8191, 29696:30207]) from blacksea as y where oid(y) = 49153 Q8 select add cells(y[8704:9215, 30720:31000]) from blacksea as y where oid(y) = 49154 Q9 select add cells(y[9216:9727, 30208:30719]) from blacksea as y where oid(y) = 49155 Table 4.3 compares the CPU cost required for the computation of the queries using pre-aggregated data and raw data. The CPU cost was obtained by using the time library of C++. The column #aff. tiles shows the number of tiles that need to be read for computing the given query. Column # preagg. tiles represents the number of preaggregates that can be used to compute the query. Column t pre shows the total CPU cost of computing the query considering pre-aggregated data. Column t ex shows the time taken to execute the query entirely from raw data. Column ratio shows that CPU time is always better when the computations consider pre-aggregated data. Table 4.3. Comparison of Query Evaluation Costs Using Pre-Aggregated Data and Original Data. Q id #aff. tiles #preagg. tiles t pre t ex ratio Q1 63 24 15.6 17.8 87% Q2 35 24 6.9 9.3 74% Q3 35 8 9.4 10 94% Q4 5 5 1.02 1.55 65% Q5 5 5 1.1 1.63 67% Q6 5 5 0.74 1.01 73% Q7 2 1 0.04 0.41 9% Q8 2 1 0.04 0.45 8% Q9 2 1 0.04 0.41 9% 4.5 Summary In this chapter we presented a framework for computing aggregate queries in array databases using pre-aggregated data. We distinguished among different types of
4.5 Summary 75 pre-aggregates: independent, overlapped, and dominant. We showed that such a distinction is useful to find a set of pre-aggregated queries that can reduce CPU cost for query computation. We proposed a cost-model to calculate the cost of using different pre-aggregates and select the best option for evaluating a query using pre-aggregated data. The measurements on real-life raster images showed that the computation of the queries is always faster with our algorithms compared to straightforward methods. We focused on queries using basic aggregate functions covering a large number of operations in GIS and remote-sensing imaging applications. The challenge remains, however, in supporting more complex aggregate operations, e.g., scaling, which is discussed in the following chapter.
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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 . . .
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List of Tables 3.1 UNO and FAO Suit
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Chapter 1 Introduction and Problem
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Relevant and complementary question
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1.2 Publications Related to this Th
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Chapter 2 Background and Related Wo
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2.1 Array Databases 17 Figure 2.2 s
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2.1 Array Databases 19 toward the s
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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: 4.4 Experimental Results 73 Query E
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
Page 119 and 120: BIBLIOGRAPHY 113 [73] Stefanovic N.
Page 121: BIBLIOGRAPHY 115 [97] Kotidis Y. an
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