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Running head: ONline analytical process 0

Online Analytical Process and Date Cube

Vaishnavi Gunnam

SEC 6050

Wilmington University

Introduction

Online Analytical Process (OLAP) is among of the powerful and potential technologies used for knowledge discovery in vast database environment. The key part of OLAP model is the data cube. It is a multidimensional arrangement of collective values which provide sophisticated model for the decision support. OLAP is the foundation for numerous business application with sales and market analysis, planning, accounting and performance evaluation. Unlike statistical databases which usually store census data and economic data, OLAP is primarily used for analyzing business data collected from daily transactions such as sales data and health care data.

The main purpose of an OLAP system is to enable analysts to construct a mental image about the underlying data by exploring it from different perspectives, at different level of generalizations, and in an interactive manner. OLAP interacts with other components, such as data warehouse and data mining, to assist analysts in making business decisions.

A data cube is a type of multidimensional structure which allows users to analyze the data that is collected from various sources for different purposes, by taking three different factors into account at same instance. Data cube was proposed as a SQL operator to support common OLAP tasks like histograms and subtotals (Wang, Jajodia, & Wijesekera, 2010) .

Uses of OLAP Data Cube

OLAP data cubes are the most advanced technology that is used to analyze the data in huge data environments. There are many applications and uses of the OLAP data cubes, the following are some of the uses of implementing OLAP data bases in various fields:

• On-Line Analytical Processing (OLAP) techniques are more progressively being used in the Decision support system in order to provide the analysis of the data. The queries that were posted on the decision support systems are very complex and need different views of data. So OLAP data cubes are used to provide various dimensional views and helps in analyzing the data for required results (Blanco et al., 2015).

• On-line Analytical Processing systems facilitate analysts and managers of the organizations to provide insight on the performance of the organization by using various different views of data for reflecting the multidimensional (Blanco et al., 2015).

• The model which is dimensional in a logical way is represented by a cube. The tools will help in facilitating the updating and maintenance of the cube which attributes the multidimensional model, which further assists in easier setting up and helps in maintaining the cube effectively from taking the assistance through the intuitions to the extent of use possible (Blanco et al., 2015).

Operations of Data Cubes

To support OLAP, the datacube should provide the following capabilities.

Roll-up

Roll-up performs aggregation on a data cube in the following ways

• By climbing up a concept hierarchy for a dimension

• By dimension reduction

The below figure illustrates the roll-up working. Roll-up is performed by climbing up a concept hierarchy for the dimension location. Initially the concept hierarchy was "street < city < province < country". On rolling up, the data is aggregated by ascending the location hierarchy from the level of city to the level of country. The data is grouped into cities rather than countries.

When roll-up is performed, one or more dimensions from the data cube are removed.

Figure 1: Image illustrating Roll-up Working.

Drill-down

Drill-down is the reverse operation of roll-up. It is performed by either of the following ways:

• By stepping down a concept hierarchy for a dimension

• By introducing a new dimension.

Figure 2: Image illustrating Drill-Down Working.

Drill-down is performed by stepping down a concept hierarchy for the dimension time. Initially the concept hierarchy was "day < month < quarter < year." On drilling down, the time dimension is descended from the level of quarter to the level of month. When drill-down is performed, one or more dimensions from the data cube are added. It navigates the data from less detailed data to highly detailed data.

Slicing and Dicing

The slice operation selects one particular dimension from a given cube and provides

new sub-cube. The new sub-cube if formed by selecting one or more dimensions. Dice selects two or more dimensions from a given cube and provides a new sub-cube. (OLAP Operations)

Pivoting

The pivot operation is also known as rotation. It rotates the data axes in view in order to provide an alternative presentation of data. According to Goli et.al (1997), in few cases the two dimensions are replaced with new dimensions in place of existing dimensions of the cube.

References

Blanco, C., Fernández-Medina, E., & Trujillo, J. (2015). Modernizing Secure OLAP Applications with a Model-Driven Approach. Computer Journal, 58(10), 2351-2367. doi:10.1093/comjnl/bxu070

Goil, S., & Choudhary, A. (January 01, 1997). High Performance OLAP and Data Mining on Parallel Computers. Data Mining and Knowledge Discovery, 1, 4, 391-417

OLAP Operations. tutorialspoint.com. Retrieved 2 October 2016, from https://www.tutorialspoint.com/dwh/pdf/dwh_olap.pdf

Wang, L., Jajodia, S., & Wijesekera, D. (2010). Preserving Privacy for On-Line Analytical Processing (OLAP). New York: Springer.