How are concept hierarchies useful in OLAP?

Data MiningDatabaseData Structure

In the multidimensional model, data are arranged into several dimensions, and each dimension includes several levels of abstraction represented by concept hierarchies. This organization supports users with the adaptability to view records from different perspectives.

Several OLAP data cube operations continue to materialize these multiple views, enabling interactive querying and analysis of the data at hand. Therefore, OLAP supports a convenient environment for interactive data analysis.

Five basic OLAP commands are used to implement data retrieval from a Data warehouse are as follows −

ROLL UP Command − The ROLL UP enables the user to summarise information into a higher general level in the hierarchy. The roll-up operation shown aggregates the records by ascending the location hierarchy from the level of the city to the level of the country. In other terms, instead of grouping the data by city, the resulting cube groups the data by country.

When roll-up is executed by dimension reduction, one or more dimensions are deleted from the given cube. For instance, consider a sales data cube including only the two dimensions location and time. Roll-up can be implemented by removing, say, the time dimension, resulting in an aggregation of the total sales by location, instead of by location and by time.

Drill-down − Drill-down is the reverse of roll-up. It operates from less detailed information to more detailed information. Drill-down can be completed by either stepping down a concept hierarchy for a dimension or presenting more dimensions. Drill-down appears by descending the time hierarchy from the level of the quarter to the more detailed level of the month. The resulting data cube analysis the total sales per month instead of summarizing them by quarter.

Slice and dice − The slice operation implemented a selection on one dimension of the given cube, resulting in a subcube. The dice operation represents a subcube by implementing a selection on two or more dimensions.

Pivot − Pivot is also known as rotate. It is a visualization operation that rotates the data axes in view to support an alternative performance of the data.

Other OLAP operations − Some OLAP systems provide more drilling operations. For instance, drill-across implements queries containing (i.e., across) more than one fact table. The drill-through operation uses relational SQL services to drill through the bottom level of a data cube down to its back-end relational tables.

Several OLAP operations can involve ranking the top N or bottom N items in lists, and calculating moving averages, growth values, and interests, internal values of return, depreciation, currency conversions, and statistical services.

Updated on 23-Nov-2021 10:25:17