2 olap operaciones
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2 olap operaciones



Operaciones OLAP

Operaciones OLAP



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    2 olap operaciones 2 olap operaciones Presentation Transcript

    • DATA WAREHOUSINGMulti DimensionalOLAP
    • Usando elDW 2
    • 3
    •  Example of two-dimensional query. ▪ What is the total revenue generated by property sales in each city, in each quarter of 2004?’  Choice of representation is based on types of queries end-user may ask.4
    • Compare representation - three-field relational table versus two- dimensional matrix.5
    •  Example of three-dimensional query.  ‘What is the total revenue generated by property sales for each type of property (Flat or House) in each city, in each quarter of 2004?’6
    • Data CubeCompare representation - four-field relational table versus three-dimensional cube.7
    •  A subset of highly interrelated data that is organized to allow users to combine any attributes in a cube (e.g., stores, products, customers, suppliers) with any metrics in the cube (e.g., sales, profit, units, age) to create various two-dimensional views, or slices, that can be displayed on a computer screen8
    •  Cube represents data as cells in an array.  Relational table only represents multi- dimensional data in two dimensions.9
    •  Use multi-dimensional structures to store data and relationships between data.  Multi-dimensional structures are best visualized as cubes of data, and cubes within cubes of data. Each side of a cube is a dimension.  A cube can be expanded to include other dimensions.10
    •  A cube supports matrix arithmetic.  Multi-dimensional query response time depends on how many cells have to be added ‘on the fly’.  As number of dimensions increases, number of the cube’s cells increases exponentially.11
    •  However, majority of multi-dimensional queries use summarized, high-level data. Solution is to pre-aggregate (consolidate) all logical subtotals and totals along all dimensions.12
    •  Pre-aggregation is valuable, as typical dimensions are hierarchical in nature.  (e.g. Time dimension hierarchy - years, quarters, months, weeks, and days)  Predefined hierarchy allows logical pre- aggregation and, conversely, allows for a logical ‘drill-down’.13
    •  Supports common analytical operations  Consolidation  Drill-down  Slicing and dicing  Pivoting14
    •  Consolidation - aggregation of data such as simple ‘roll-ups’ or complex expressions involving inter-related data. Drill-Down - is the reverse of consolidation and involves displaying the detailed data that comprises the consolidated data.  The investigation of information in detail (e.g., finding not only total sales but also sales by region, by product, or by salesperson). Finding the detailed sources.15
    • 16
    •  Slicing and Dicing: refers to the ability to look at the data from different viewpoints.  dice: to cut into small cubes  slice: A section of an cube selected by specifying its lower and upper limitsslice(color,mes) dice(color) 17
    •  Pivoting:  Pivot deals with presentation  Choose some dimensions X1, . . . ,Xi to appear on x and some dims Y1, . . . ,Yj to appear on y.18
    •  Can store data in a compressed form by dynamically selecting physical storage organizations and compression techniques that maximize space utilization.  Dense data (that is, data that exists for a high percentage of cells) can be stored separately from sparse data (that is, a significant percentage of cells are empty).19
    •  Ability to omit empty or repetitive cells can greatly reduce the size of the cube and the amount of processing.  Allows analysis of exceptionally large amounts of data.20
    •  In summary, pre-aggregation, dimensional hierarchy, and sparse data management can significantly reduce the size of the cube and the need to calculate values ‘on-the-fly’.  Removes need for multi-table joins and provides quick and direct access to arrays of data, thus significantly speeding up execution of multi-dimensional queries.21
    •  Efraim Turban. Business Intelligence. Prentice Hall.2008.