Data Mining_ Concepts and Techniques - Jiawei Han [93]
Most large data cube applications require efficient computation of distributive and algebraic measures. Many efficient techniques for this exist. In contrast, it is difficult to compute holistic measures efficiently. Efficient techniques to approximate the computation of some holistic measures, however, do exist. For example, rather than computing the exact median(), Equation (2.3) of Chapter 2 can be used to estimate the approximate median value for a large data set. In many cases, such techniques are sufficient to overcome the difficulties of efficient computation of holistic measures.
Various methods for computing different measures in data cube construction are discussed in depth in Chapter 5. Notice that most of the current data cube technology confines the measures of multidimensional databases to numeric data. However, measures can also be applied to other kinds of data, such as spatial, multimedia, or text data.
4.2.5. Typical OLAP Operations
“How are concept hierarchies useful in OLAP?” In the multidimensional model, data are organized into multiple dimensions, and each dimension contains multiple levels of abstraction defined by concept hierarchies. This organization provides users with the flexibility to view data from different perspectives. A number of OLAP data cube operations exist to materialize these different views, allowing interactive querying and analysis of the data at hand. Hence, OLAP provides a user-friendly environment for interactive data analysis.
OLAP operations
Let's look at some typical OLAP operations for multidimensional data. Each of the following operations described is illustrated in Figure 4.12. At the center of the figure is a data cube for AllElectronics sales. The cube contains the dimensions 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 item types. To aid in our explanation, we refer to this cube as the central cube. The measure displayed is dollars_sold (in thousands). (For improved readability, only some of the cubes' cell values are shown.) The data examined are for the cities Chicago, New York, Toronto, and Vancouver.
Roll-up: The roll-up operation (also called the drill-up operation by some vendors) performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension or by dimension reduction. Figure 4.12 shows the result of a roll-up operation performed on the central cube by climbing up the concept hierarchy for location given in Figure 4.9. This hierarchy was defined as the total order “street When roll-up is performed by dimension reduction, one or more dimensions are removed from the given cube. For example, consider a sales data cube containing only the location and time dimensions. Roll-up may be performed by removing, say, the time dimension, resulting in an aggregation of the total sales by location, rather than by 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. Figure 4.12 shows the result of a drill-down operation performed on the central cube by stepping down a concept hierarchy for time defined as “day