Querying Multidimensional Data-Data Warehouse-Lecture Handout, Exercises for Data Warehousing. Baddi University of Emerging Sciences and Technologies
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sharib_sweet8 August 2012

Querying Multidimensional Data-Data Warehouse-Lecture Handout, Exercises for Data Warehousing. Baddi University of Emerging Sciences and Technologies

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Topics include in this course are Data Warehousing Concepts, Design and Development, Extraction, Transformation and Loading, OLAP Technology, Data Mining Techniques: Classification, Clustering and Decision Tree, Advanced...
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OLAP operations: Querying Multidimensional Data In the multidimensional model, data are organized into multiple dimensions, and each dimension contains multiple levels of abstraction defined by the hierarchies. This organization provides users with the ability to view data from different perspectives. A number of data cube operations exist to materialize the different views, allowing interactive querying and analysis of the data. Following are some typical OLAP operations for multidimensional data. Let us take an example of a cube containing the dimensions of location, time, and item, where location is aggregated with respect to city values, time is aggregated with respect to quarters, and item is aggregated with respect to types.Roll-Up: The roll-up (or drill-up) operation performs aggregation on a data cube, either by climbing up a data hierarchy for a dimension or by dimension reduction. Roll-up by dimension reduction means that aggregation is performed up to the top level of a dimension. For example, if the location hierarchy contains three levels, city -> state -> country, then reduction of location dimension means, the resulting fact data will be summed over the city, and then over the states. Drill-Down: Drill-down is the reverse of roll-up. It navigates from less detailed data to more detailed data. It can be done either stepping down a hierarchy for a dimension or introducing additional dimensions. Adding a new dimension means the fact table must contain (or be added) data in that dimension. Slice and Dice: The slice operation performs a selection on one dimension of the given cube, resulting in a subcube. For example, we can select all sales data for various cities and items for a particular quarter = Q1. The dice operation defines a subcube by performing a selection on two or more dimensions. For example, we can first slice on time to include sales for some quarters, and then on location to include sales of some cities. Pivot (Rotate): Pivot is a visualization operation that rotates the data axes (in view) in order to provide an alternative presentation of the data.

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