About BI-SSAS

1. What is it? SSAS is a component used for SQL Server databases for BI. With SSAS, you can create multidimensional databases and perform data mining on them. This document describes how to analyze SSAS data. Next let's take a look at it. The solutions provided by business intelligence can obtain data from multiple data sources and

1. What is it? SSAS is a component used for SQL Server databases for BI. With SSAS, you can create multidimensional databases and perform data mining on them. This document describes how to analyze SSAS data. Next let's take a look at it. The solutions provided by business intelligence can obtain data from multiple data sources and

1. What is it?

SSAS is a component used for SQL Server databases for BI. With SSAS, you can create multidimensional databases and perform data mining on them. This document describes how to analyze SSAS data. Next let's take a look at it.

The solutions provided by business intelligence can obtain data from multiple data sources and convert various data into data in the same format for storage, so that users can quickly access and interpret data, to provide effective data support for user analysis and decision-making, SSAS provides faster and more advanced data mining for data analysis by establishing multi-dimensional datasets.

Ii. Structure

SSAS, also known as the mining structure, defines the data based on when the mining model is generated: it specifies the source data view, the number and type of columns, and the optional partitions divided into the train set and test set. A single mining structure supports multiple mining models that share the same domain. Describes the relationship between the data mining structure and data sources and the data mining model.

Data Processing: Source-to-structure-to-Model

The mining structure in the relationship diagram is based on data sources that contain multiple tables or views. They are joined by the CustomerID field. A table contains information about the customer, such as geographical region, age, income, and gender, and a nested table contains multiple rows of other related information for each customer, such as the product that the customer has purchased. This graph shows that multiple models can be generated based on a mining structure, and these models can use different columns in this structure.

Model 1 uses mermerid, income, age, and region, and filters data by region.

Model 2 uses mermerid, income, age, and region, and filters data by age.

Model 3 uses a CustomerID, age, gender, and nested table without a filter.

Because the above models use different input columns and the two models use filters to restrict the data used in the model, even if these models are based on the same data, the results are also very different. Note that the CustomerID column is required in all models because it is a unique available column that can be used as a case key.

The above description shows the basic architecture of the data mining structure: how to define the mining structure, how to fill it with data, and how to use it to create a model. The next article will be implemented through a simple instance.

Iii. Advantages and Disadvantages

(1) data mining uses carefully studied statistical principles to discover patterns in your data and help you make informed decisions on complex problems. By applying data mining algorithms in Analysis Services to your data, you can predict trends, identify patterns, create rules and recommendations, analyze event sequences in complex datasets, and gain insight into new situations.

(2) Data Mining in SQL Server 2014 is not only powerful and easy to access, but also integrated with tools that many prefer when performing analysis and reporting. By viewing the links provided in this section, you can obtain rich background information you need to learn about data mining.

Iv. Summary

Through the simple introduction above, we have a certain understanding of SSAS. As for its implementation and various details, such as data source creation, data view creation, and multi-dimensional dataset, the next article, such as multi-dimensional creation, will explain through simple examples.