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

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28 2. Background and Related Work aggregate queries [68]. One difficulty that MOLAP poses, however, pertains to the sparseness of the data. Sparseness means that many events did not take place and valuable processing time is taken by adding up zeros [91]. For example, a company may not sell every item every day in every store, so no values appear at the intersection where products are not sold in a particular region at a particular time. On the other hand, MOLAP can be much faster for applications where subsets of the data cube are dense [100]. Another limitation of this approach is that the computation of a cube requires a complex aggregate query across all data in a warehouse. Though it is possible to incrementally update cubes as new data arrives, it is impractical to dynamically create new cubes to answer ad-hoc queries [68]. Figure 2.10. MOLAP Storage Scheme ROLAP In ROLAP, underlying data is stored in a relational database, see Fig. 2.11(a). The relational model, however, does not include concepts of dimension, measure, and hierarchy. Thus specific types of schemata must be created so the multidimensional model can be represented in terms of basic relational elements such as attributes, relations, and integrity constraints [68]. Such representations are done using a star schema data model, although the snowflake schema is also often adopted. ROLAP implementations can handle large amounts of data and leverage all functionalities of the relational database [72]. Disadvantages are that overall performance is slow and each ROLAP report represents an SQL query with the limitations of the genre. ROLAP vendors tried to mitigate this problem by including out-of-the-box complex functions in their product offering and providing users the capability of defining their own functions. Another problem with ROLAP implementations results from the performance hit caused by costly join operations between large tables [68]. To overcome this issue, fact tables in data-warehouses are usually de-normalized. Sub-
2.2 On-Line Analytical Processing (OLAP) 29 stantial performance gains can be achieved through the materialization of derived tables (views) that store aggregate data used for typical OLAP queries. Figure 2.11. ROLAP Storage Scheme Figure 2.12 shows the formulation of a typical query in both ROLAP and MOLAP. The query yields sales information for a specific product sold in a particular city by a given vendor. The formulation of the queries is done according to the syntax of Oracle 10g. Note the lengthy difference between the two query formulations. (a) Sample ROLAP query (b) Sample MOLAP query Figure 2.12. Typical Query as Expressed in ROLAP and MOLAP Systems
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 (
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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
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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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