Olap Data Cube Operations PDF

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Olap Data Cube Operations...

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OLAP Data Cube Operations Harshit Rawat July 21, 2018 Wilmington University

The term Online Analytic Processing, also referred to as the OLAP and data warehousing go hand in hand and more often than not tend to have a direct and huge impact on the decision

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support systems and the business intelligence systems of the organizations. OLAP systems are primarily designed and developed to help the organizations in data warehousing large heaps of data and also to analyze and interpret the data of the organization in a more effective and efficient manner. The dimensional modeling in the data warehousing that allows the organizations to analyze the data effectively is the support that they get from using OLAP. This efficiency allows for a greater category of business intelligence like relational database, data mining and report writing (Rouse, 2012). Sales reporting, marketing, business process management, forecasting, budgeting, creating finance reports and many others are some of the basic applications and functionalities of the OLAP. To put it in simple words, OLAP data cube can be summarized as a method of storing data in a multidimensional form, that is more often than not used for the purpose of reporting. In OLAP data cubes, data is primarily categorized by the measure of dimensions. OLAP cubes are often pre-summarized across dimensions to drastically improve query time over relational databases. The query language that is used to interact with the OLAP data cubes and perform multiple tasks is called as the multidimensional expressions also known as the MDX (Rouse, 2012). The origin of MDX can be traced back to the early 1990’s and it was first founded by a technology giant Microsoft. Since its introduction, MDX has been adopted by millions of vendors that are the users of the multidimensional database. Out of the many of its one features, of the OLAP data cube, it stores data like a traditional database even though it is very much different in the structure. In contrast to the traditional databases which are designed in accordance to the very specific requirements of the information technology systems that use them, whereas the OLAP cubes are however, primarily used by business owners for advanced analytics. The basic infrastructure of the OLAP data cubes

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is designed keeping in mind the business logic and the primary usage of the business owner. The OLAP data cubes are specifically optimized for analytical purposes such that they can be used to generate report on millions of records of time. To make it simpler for business owners to operate, business owners can query OLAP data cubes using plain English. OLAP is known to provide a very convenient and user friendly environment for the use of interactive data analysis. A number of OLAP data cube operations exist to materialize different views of data, allowing interactive querying and analysis of data (Rouse, 2012). One major operation of the OLAP data cube is a roll up operation. A roll up operation is also known as the drill up or aggregation operation which is used to perform aggregation operations on a data cube, which is either by climbing up a concept hierarchy for a dimension or by climbing down a concept hierarchy, which is also called as the dimension reduction (Perficient, 2017). Another very useful operation in the OLAP data cube is the slice operation. A slice in a multidimensional array is a column of data corresponding to a single value for one or more members of the dimension. In other words, it helps the users and business owners to visualize the information and gather information that is very specific to the dimension. Slicing operation can be visualized as a specific filter for a particular value in a dimension (Perficient, 2017). Yet another one of the OLAP data cube operation is the dicing function. Dicing is similar to slicing, but a little more different in functionality. When a person thinks of slicing operation, filtering is done to focus on a particular attribute. Whereas dicing on the other hand, is more of a zoom feature that selects a subset over all the dimensions, but for specific values of the dimension (Perficient, 2017). Drill down is another operation for the OLAP data cube. It is actually the complete opposite of the roll up operation of the OLAP data cube operation. In the drill down

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operation, the data is gathered and aggregated from a higher level summary to a lower level summary or detailed data (Perficient, 2017).

References

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M. Rouse., (April, 2017). OLAP cube. Retrieved from: https://searchdatamanagement.techtarget.com/definition/OLAP-cube PERFICIENT., (2017, August 02). Data Cubes Operations - SQL queries. Retrieved from: https://blogs.perficient.com/2017/08/02/data-cube-operations-sql-queries/...