What are the OLAP operations in data mining?

OLAP stands for On-Line Analytical Processing. OLAP is an element of software technology that authorizes analysts, managers, and executives to gain insight into data through fast, consistent, interactive access in a wide variety of possible views of data that has been changed from raw information to reflect the actual dimensionality of the enterprise as learned by the client.

OLAP servers present business users with multidimensional information from data warehouses or data marts, without concerns about how or where the data are saved. The physical structure and execution of OLAP servers should consider data storage issues.

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.

Slice − It describes the subcube to get more specific information. This is performed by selecting one dimension.

Dice − It describes the subcube by performing selection on two or more dimensions.

Roll-up − The roll-up enables the user to summarise information into a higher general level in the hierarchy. The roll-up operation shown aggregates the data by growing the area 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 executed by deleting 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 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 arrives by descending the time hierarchy from the level of the quarter to the accurate level of the month. The resulting data cube analysis the total sales per month instead of summarizing them by quarter.

Visualisation − Visualization refers to the visual representation of data with the help of comprehensive charts, images, lists, charts, and other visual objects. It allows users to simply understand the data within a fraction of time and extract useful data, patterns, and trends. Furthermore, it creates the data easy to understand.

In other words, it can say that data representation in graphical structure so that users can simply comprehend the process of trends in the data is known as data visualization.

Updated on: 15-Feb-2022

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