The SSAS modeling tools provided in the SQLServer 2008 R2 database include SQL Server Management Studio and Business Intelligence Development Studio. So what is the modeling process? In this article, let’s take a look at the modeling tools and key processes of SQLServer 2008 R2 database, as well as data mining, permissions and access interfaces.
Modeling tools and critical processes
The purpose of SSAS modeling is to design multidimensional database objects. Modeling tools include SQL Server Management Studio and Business Intelligence Development Studio. The former is used to manage instances of Analysis Services, SQL Server, Integration Services and Reporting Services. It can manage Analysis Services objects (execute backups, processing, etc.). It can also use XMLA scripts to create new objects directly on existing Analysis Services instances, and provides analysis server script projects. Business Intelligence Development Studio is a development environment based on Visual Studio 2008 for creating and modifying business intelligence solutions. Using Business Intelligence Development Studio, you can create Analysis Services projects that contain the definition of Analysis Services objects (cubes, dimensions, etc.).
There are four key steps to modeling with Business Intelligence Development Studio:
(1) Define the Analysis Services project.
(2) Configure Analysis Services project properties.
(3) Generate Analysis Services project.
(4) Deploy the Analysis Services project.
SSAS database extension capabilities, including: data mining, permissions and access interfaces
Compared with traditional OLAP platforms, SSAS provides more advanced features. This allows organizations to leverage one solution to meet multiple analytical needs, as the solution offers much more features than traditional OLAP platforms. In this regard, Unified Dimensional Model plays a core role, which provides rich analytical capabilities.
Unified Dimensional Model (UDM) is a new concept for Analysis Services, which first emerged with the release of SQL Server 2005. It provides an intermediate logical layer between a physical relational database used as a data source and a proprietary cube and a dimensional structure used to satisfy user queries. In this way, UDM can be regarded as the core part of the OLAP solution. The model also offers a wealth of advanced business intelligence capabilities to provide optimal relationship analysis and OLAP analysis and further enables organizations to easily scale solutions with the unique Key Performance Indicator Framework and complex predictive analytics capabilities. SSAS is not only easy to extend the solution, but also extends data mining, permissions and access interfaces. The following are described separately:
(1) Data mining extension
SASS' extension of data mining is mainly reflected in: providing a set of industry-standard data mining algorithms; through the data mining designer, it is possible to create, manage and browse data mining models, and then use these models to create predictions; it supports the Data Mining Extension Plug-in (DMX) language, which can be used to manage mining models and create complex prediction queries.
These functions and tools provide effective extensions to data mining. You can use a certain function or tool alone, or you can use these functions and tools in combination to discover trends and patterns in the data and make data support for decisions.
(2) Permission expansion
The protection of SASS logarithm is divided into two levels: instance level and user level. The instance level consists of all physical elements used by an Analysis Services instance and must be protected to ensure that only authorized users have access to them. These elements include data folders, applications, and more. The user-level is composed of permissions granted to the user, which allows the user to access information stored in the Analysis Services database and prevents the user from accessing data beyond their privileges. The user-level permissions are implemented in the following ways:
- Establish a user authentication mechanism
- User permissions that define server roles
- Define OLAP object-level security
- Define data mining object-level security
- Define assembly and stored procedure-level security
- Enable or disable instance configuration properties
(3) Access interface extension
During the process of using SSAS to develop reports or perform data mining, the extensions of the access interface include: OLE DB for Data Mining, Analysis Management Objects (AMO), Analysis Services scripting language.
OLE DB for Data Mining extends the Microsoft OLE DB for Data Mining 1.0 specification to add new schema row sets, add columns in existing schema row sets, and add syntax to the Data Mining Extension Plug-in (DMX) language to create and manage mining structures.
Analytical Management Objects (AMO) is a complete library of objects that are programmatically accessible that enables applications to manage running instances of Microsoft SQL Server Analysis Services. Is a Microsoft .NET Framework data access interface for communicating with Microsoft SQL Server Analysis Services. You can use the XML for Analysis protocol to communicate with analytics data sources by using a TCP/IP or HTTP connection to transmit and receive SOAP requests and responses that comply with the XML for Analysis specification.
Analysis Services Script language (ASSL) is a script language used by SASS client applications to communicate with Analysis Services. It is a special XML language, including object definition language and command language that sends operation commands to Analysis Services instances.
This is the end of the introduction to the knowledge about SQLServer 2008 R2 database SSAS modeling. I hope this introduction can bring you some benefits.