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ICONS International Journal of Computer Science and Network Security, VOL.8 No.10, October 2008319Data Cube Representation of Patient Registration System through UML Dr. Vi pin SABENA, Gunman Ahmad

How to fill out data cube representation of

When filling out a data cube representation, it is important to follow a step-by-step process to ensure accuracy and clarity. The following points outline the steps involved in filling out a data cube representation:

1. Identify the dimensions: Begin by identifying the dimensions of the data cube. These dimensions represent the categories or attributes by which the data will be categorized. For example, if analyzing sales data, the dimensions could include time, region, and product category.
2. Determine the measures: Next, determine the measures or metrics that will be used to quantify the data within the data cube. Measures can include numerical values such as sales revenue, quantity sold, or average customer rating.
3. Define the hierarchy: Establish hierarchical relationships within the dimensions. This involves organizing the dimensions in a structured manner, typically from the most general level to the most specific level. For example, within the time dimension, you could have a hierarchy of year, quarter, month, and day.
4. Populate the data cube: Input the actual data into the data cube representation. This involves assigning values to the intersections of the dimensions and measures. For example, if analyzing sales data, you would input the sales revenue for each combination of the dimensions (e.g., sales revenue for a specific product category in a particular region during a particular quarter).
5. Analyze the data: Once the data cube representation is filled out, it can be used for analysis. By examining different combinations of dimensions and measures, you can gain insights and identify patterns or trends within the data. This analysis can help in making informed decisions, identifying opportunities, or solving problems.

Who needs data cube representation of:

1. Business Analysts: Data cube representations are essential tools for business analysts. They allow analysts to easily analyze and understand complex data sets, identify trends or patterns, and make informed decisions based on the insights gained.
2. Data Scientists: Data cube representations can be highly valuable for data scientists who are involved in data modeling, analysis, and prediction. The structured nature of data cube representations provides a clear and organized way to comprehend and manipulate large-scale datasets.
3. Database Administrators: Database administrators utilize data cube representations to optimize database performance and query execution. By understanding the structure of the data cube, they can design efficient schemas and indexing strategies to ensure fast and accurate data retrieval.

In summary, filling out a data cube representation involves identifying dimensions, determining measures, defining hierarchies, populating the data, and analyzing the results. Business analysts, data scientists, and database administrators are among those who can greatly benefit from this representation in their work.

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What is data cube representation of?

Data cube representation is a multidimensional structure that allows the efficient storage and analysis of data.

Who is required to file data cube representation of?

The individuals or organizations who collect and analyze large volumes of data may be required to file data cube representation.

How to fill out data cube representation of?

Filling out data cube representation involves organizing the data into dimensions, hierarchies, and measures, and populating the cube with the relevant data points.

What is the purpose of data cube representation of?

The purpose of data cube representation is to provide a structured and efficient way to analyze and query large volumes of data from different perspectives.

What information must be reported on data cube representation of?

The specific information to be reported on a data cube representation depends on the data being analyzed, but it typically includes dimensions, measures, and hierarchies.