Open shuijian-xu opened 5 years ago
It was noticed quite early on when data warehouses started to be developed that, whenever decision makers were asked to describe the kinds of questions they would like to get answers to regarding their organizations, they almost always wanted the following:
Summarized information with the ability to break the summaries into more detail
Analysis of the summarized information across their own organizational components such as “departments” or “regions”
Ability to “slice and dice” the information in any way they chose
Display of the information in both graphical and tabular form
Capability to view their information over time
So as an example, they might wish to see a report showing Wine Sales by Product, or a report showing Sales by Customer, or even Sales by Product by Customer
There are typically a number of different dimensions from which a given pool of data can be analyzed. This plural perspective, or multidimensional conceptual view, appears to be the way most business persons naturally view their enterprise.
There are typically a number of different dimensions from which a given pool of data can be analyzed. This plural perspective, or multidimensional conceptual view, appears to be the way most business persons naturally view their enterprise.
In the example above, the subject area would be Sales. The dimensions of analysis would be Customers and Products. The requirement is to analyze sales by customer and sales by product.
Sales can be analyzed by Customer by Product over Time.
Subject: Sales Dimension: Customer, Product, Time
Sales by Product, Sales by Customer, Sales by Area, Sales by Time
One approach to data warehouse design is to develop and implement a “dimensional model.” This has given rise to dimensional analysis (sometimes generalized as multi-dimensional analysis ).