 Roll-up (Drill-up): The roll-up operation performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension or by dimension reduction.

• Performing roll-up using climbing up a concept hierarchy :

o Consider a hierarchy defined as the total order “street<city <province="" or="" state="" <country.”="" <="" p="">

o Rather than grouping the data by city, the resulting cube groups the data by country.

• Performing roll-up using dimension reduction:

o One or more dimensions are removed from the given cube.

o Consider a sales data cube containing only the two dimensions location and time.

o Roll-up may be performed by removing the time dimension, resulting in an aggregation of the total sales by location, rather than by both location and by time.

 Drill-down: Drill-down is the reverse of roll-up. It navigates from less detailed data to more detailed data. Drill-down can be realized by either stepping down a concept hierarchy for a dimension or introducing additional dimensions.

• Performing a drill-down operation using stepping down a concept hierarchy

o Consider time defined as “day <month <quarter="" <year.”="" <="" p="">

o Drill-down occurs by descending the time hierarchy from the level of quarter to the more detailed level of month.

• Performing a drill-down operation by adding new dimensions to a cube

o Consider the central cube of the figure

o A drill-down on can occur by introducing an additional dimension, such as customer group.

 Slice: The slice operation performs a selection on one dimension of the given cube, resulting in a sub cube.

o The figure shows a slice operation where the sales data are selected from the central cube for the dimension ‘time’ using the criterion ‘time= “Q1” ’.

 Dice: The dice operation defines a sub cube by performing a selection on two or more dimensions.

o The figure shows a dice operation on the central cube based on the following selection criteria that involve three dimensions: (location = “Toronto” or “Vancouver”) and (time = “Q1” or “Q2”) and (item = “home entertainment” or “computer”).

 Pivot (rotate): Pivot is a visualization operation that rotates the data axes in view in order to provide an alternative presentation of the data.

o The figure shows a pivot operation where the item and location axes in a 2-D slice are rotated.

o Other examples include rotating the axes in a 3-D cube, or transforming a 3-D cube into a series of 2-D planes.

 Other OLAP operations( extra points for reference)

• Drill-across operationexecutes queries involving more than one fact table.

• Drill-through operation uses relational SQL facilities to drill through the bottom level of a data cube down to its back-end relational tables.

• ranking the top N or bottom N items in lists, as well as computing moving averages, growth rates, interests, internal rates of return, depreciation, currency conversions, and statistical functions.

• OLAP offers analytical modeling capabilities, including a calculation engine for deriving ratios, variance, and so on, and for computing measures across multiple dimensions.

• It can generate summarizations, aggregations, and hierarchies at each granularity level and at every dimension intersection.

• OLAP also supports functional models for forecasting, trend analysis, and statistical analysis. In this context, an OLAP engine is a powerful data analysis tool.

APPLICATION OF OLAP:

OLAP is widely used in several realms of data management. Some of these applications include: -

1. Financial Applications

• Activity-based costing (resource allocation)

• Budgeting

1. Marketing/Sales Applications

• Market Research Analysis

• Sales Forecasting

• Promotions Analysis

• Customer Analyses

• Market/Customer Segmentation

1. Business modeling

• Simulating business behavior

• Extensive, real-time decision support system for managers.