Online Analytical Process 2 And Data

Online Analytical Process 2 Online Analytical Process and Data Cube Vaishnavi Gunnam SEC 6050 Wilmington University

Online Analytical Process (OLAP) is among the powerful and potential technologies used for knowledge discovery in vast database environments. The key component of the OLAP model is the data cube, a multidimensional arrangement of collective values that provides a sophisticated model for decision support. OLAP forms the foundation for numerous business applications, including sales and market analysis, planning, accounting, and performance evaluation. Unlike statistical databases that usually store census or economic data, OLAP primarily analyzes business data collected from daily transactions, such as sales data and healthcare information.

The main purpose of an OLAP system is to enable analysts to construct mental images of the underlying data by exploring it from multiple perspectives, at different levels of generalization, and interactively. OLAP interacts with other components like data warehouses and data mining tools to assist analysts in making informed business decisions. A data cube is a type of multidimensional structure that allows users to analyze data from various sources for different purposes, considering three different factors simultaneously. It was proposed as an SQL operator to support common OLAP tasks such as generating histograms and subtotals (Wang, Jajodia, & Wijesekera, 2010).

Uses of OLAP Data Cube

OLAP data cubes are the most advanced technology used to analyze data in large environments. They have vast applications across various fields, including:

• Decision support systems: OLAP techniques are increasingly used to analyze complex queries by providing various views of data, enabling decision-makers to extract relevant information efficiently (Blanco et al., 2015).
• Performance analysis: Managers utilize OLAP to assess organizational performance through multiple data perspectives, facilitating a comprehensive understanding of business dynamics (Blanco et al., 2015).
• Data maintenance: The logical dimensional model represented by cubes simplifies updating and management processes and supports effective maintenance through intuitive tools (Blanco et al., 2015).

Operations of Data Cubes

To support OLAP, data cubes should provide several capabilities:

Roll-up

Roll-up performs aggregation on a data cube either by climbing up a concept hierarchy for a specific dimension or by reducing the number of dimensions. For example, rolling up geographic data from city to country involves aggregating data at the higher level, providing summarized information. This operation helps in viewing data at broader levels of detail, facilitating strategic analysis (Wang, Jajodia, & Wijesekera, 2010).

Drill-down

Drill-down is the reverse of roll-up; it involves descending a concept hierarchy or adding dimensions to analyze data in more detail. For instance, from a quarterly sales view, drilling down to monthly data provides more granular information necessary for tactical decisions (Wang, Jajodia, & Wijesekera, 2010).

Slicing and Dicing

Slicing involves selecting a particular dimension to create a sub-cube, while dicing involves selecting multiple dimensions to form a more specific sub-cube. These operations allow analysts to focus on specific segments of data for detailed examination.

Pivoting

Also called rotation, pivoting changes the axes in the view to provide an alternative perspective of the data. It enables analysts to explore data relationships dynamically by repositioning dimensions, thus revealing hidden patterns or trends (Goli et al., 1997).

References

• Blanco, C., Fernández-Medina, E., & Trujillo, J. (2015). Modernizing Secure OLAP Applications with a Model-Driven Approach. Computer Journal, 58(10). https://doi.org/10.1093/comjnl/bxu070
• Goli, S., & Choudhary, A. (1997). High Performance OLAP and Data Mining on Parallel Computers. Data Mining and Knowledge Discovery, 1(4).
• Wang, L., Jajodia, S., & Wijesekera, D. (2010). Preserving Privacy for On-Line Analytical Processing (OLAP). New York: Springer.