Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube

Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube

Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube

Ta có thể tạo các tính toán phức tạp trên cube thông qua calculation.

Ta có thể tạo các tính toán phức tạp trên cube thông qua calculation.

Date 13/800: thứ 2 14-10-2013: tạo calculation từ cube

https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiuh61LhX0k1Y-b_kGxY4LCUwIO2EIpVQAIvjeMQ06T_3WfAm8FID-cd0UklTyVTyluM4LPXaZnKVcjthyOBO_yuXQV-bRtEH1KttE73rcXgQrgpugBljvfHdXEmAQHLL951_QgeBFhb3s/s72-c/create-calculation-cube-ssas.png

Date 12/800

Date 12/800

Date 12/800

Sự khác biệt giữa Star schema và snowflake schema

In a star schema, you create dimensions from single
tables joined to a fact table. In a snowfl ake schema, you create dimensions from two or more
joined dimension tables.

Sự khác biệt giữa Star schema và snowflake schema

In a star schema, you create dimensions from single
tables joined to a fact table. In a snowfl ake schema, you create dimensions from two or more
joined dimension tables.

Date 12/800

Date 12/800: thứ hai 7-10-2013: BI semantic model

Date 12/800: thứ hai 7-10-2013: BI semantic model

Date 12/800: thứ hai 7-10-2013: BI semantic model

http://www.mssqltips.com/sqlservertip/2818/understanding-the-sql-server-2012-bi-semantic-model-bism/

Đại khái là nó giống như 1 nền tảng BI thống nhất, unified BI flatform, cho phép người dùng có thể dùng multidimensional model hay tabular model, kết hợp những công nghệ hiện tại và công nghệ mới

--------------

SSAS 2012 cung cấp 3 cách tiếp cận để xây dựng BI semantic model: multidimensional, tabular and power pivot.

http://msdn.microsoft.com/en-us/library/hh212940(v=sql.110).aspx

BISM được xây dựng như 1 model thống nhất cho mọi đối tượng người dùng, từ user đến developer.

http://www.codeproject.com/Articles/506032/WhatplusisplusBIplusSemanticplusmodelplus-BISM-plu

http://www.mssqltips.com/sqlservertip/2818/understanding-the-sql-server-2012-bi-semantic-model-bism/

Đại khái là nó giống như 1 nền tảng BI thống nhất, unified BI flatform, cho phép người dùng có thể dùng multidimensional model hay tabular model, kết hợp những công nghệ hiện tại và công nghệ mới

--------------

SSAS 2012 cung cấp 3 cách tiếp cận để xây dựng BI semantic model: multidimensional, tabular and power pivot.

http://msdn.microsoft.com/en-us/library/hh212940(v=sql.110).aspx

BISM được xây dựng như 1 model thống nhất cho mọi đối tượng người dùng, từ user đến developer.

http://www.codeproject.com/Articles/506032/WhatplusisplusBIplusSemanticplusmodelplus-BISM-plu

Date 12/800: thứ hai 7-10-2013: BI semantic model

Date 11/800

Date 11/800

Date 11/800

Measures

If you had a calculated measure that returned the percentage of sales along some dimension, what type of measure would this be?

Choose your answer:

Additive Semiadditive Nonadditive Partial

Measures

If you had a calculated measure that returned the percentage of sales along some dimension, what type of measure would this be?

Choose your answer:

Additive Semiadditive Nonadditive Partial

Date 11/800

Date 11/800: chủ nhật 6-10-2013

Date 11/800: chủ nhật 6-10-2013

Date 11/800: chủ nhật 6-10-2013

MDX: multi dimensional eXpression:

Just as Structured Query Language (SQL) is a query language used to retrieve data from relational

databases, Multi-Dimensional eXpressions (MDX) is a query language used to retrieve data from

Analysis Services databases. MDX supports two distinct modes:

‰ Expressions language: Defi ne and manipulate Analysis Services objects and data to calculate

values.

‰ Query language: Retrieve data from Analysis Services.

MDX was originally designed by Microsoft and introduced in 1998 with SQL Server Analysis

Services 7.0, but it is nevertheless a general, standards-based query language to retrieve data from

OLAP databases. Many other OLAP providers support MDX, including Microstrategy’s Intelligence

Server, Hyperion’s Essbase Server, and SAS’s Enterprise BI Server. There are those who want to

extend the standard for additional functionality, and MDX extensions have indeed been developed

by individual vendors, but the constituent parts of any extension are expected to be consistent with

the MDX standard. Analysis Services provides several extensions to the MDX standard defi ned by

the OLE DB for OLAP specifi cation. In this book you learn about the form of MDX supported by

SQL Server Analysis Services 2012.

Khái niệm về Measure Group
Measure Groups: Measure groups are composed of one or more columns of a fact
table that, in turn, are composed of the data to be aggregated and analyzed. Measure
groups combine multiple measures under a single entity.

Business intelligence is nothing more than analyzing your data and making
actionable decisions.

Trả lời 1 câu hỏi liên quan đến Analysis services, nó hỏi ssas có thể chạy trong mode nào, tôi trả lồi windows authentication và nó đã đúng,
tham khảo: http://msdn.microsoft.com/en-us/library/ms144288(SQL.90).aspx

MDX: multi dimensional eXpression:

Just as Structured Query Language (SQL) is a query language used to retrieve data from relational

databases, Multi-Dimensional eXpressions (MDX) is a query language used to retrieve data from

Analysis Services databases. MDX supports two distinct modes:

‰ Expressions language: Defi ne and manipulate Analysis Services objects and data to calculate

values.

‰ Query language: Retrieve data from Analysis Services.

MDX was originally designed by Microsoft and introduced in 1998 with SQL Server Analysis

Services 7.0, but it is nevertheless a general, standards-based query language to retrieve data from

OLAP databases. Many other OLAP providers support MDX, including Microstrategy’s Intelligence

Server, Hyperion’s Essbase Server, and SAS’s Enterprise BI Server. There are those who want to

extend the standard for additional functionality, and MDX extensions have indeed been developed

by individual vendors, but the constituent parts of any extension are expected to be consistent with

the MDX standard. Analysis Services provides several extensions to the MDX standard defi ned by

the OLE DB for OLAP specifi cation. In this book you learn about the form of MDX supported by

SQL Server Analysis Services 2012.

Khái niệm về Measure Group
Measure Groups: Measure groups are composed of one or more columns of a fact
table that, in turn, are composed of the data to be aggregated and analyzed. Measure
groups combine multiple measures under a single entity.

Business intelligence is nothing more than analyzing your data and making
actionable decisions.

Trả lời 1 câu hỏi liên quan đến Analysis services, nó hỏi ssas có thể chạy trong mode nào, tôi trả lồi windows authentication và nó đã đúng,
tham khảo: http://msdn.microsoft.com/en-us/library/ms144288(SQL.90).aspx

Date 11/800: chủ nhật 6-10-2013

Date 10/800: thứ 6 04-10-2013

Date 10/800: thứ 6 04-10-2013

Date 10/800: thứ 6 04-10-2013

Data Sources: Your data warehouse is likely made up of disparate data sources such as
Microsoft SQL Server, Oracle, DB2, Teradata, and so forth. Analysis Services can easily deal
with retrieving relational data from various relational databases. Data source objects contain
information needed to connect to a data source such as server name, catalog or database
name, and login credentials. You establish connections to relational servers by creating a data
source for each one.
‰ Data Source Views: When working with a large operational data store, you don’t always
want to use all the tables in the database. With Data Source Views (DSVs), you can limit the
number of visible tables by including only the tables that are relevant to your analysis. DSVs
enable you to create a logical data model upon which you build your multidimensional database.
A DSV can contain tables from one or more data sources. Data sources and DSVs are
discussed in Chapter 4.

Data Sources: Your data warehouse is likely made up of disparate data sources such as
Microsoft SQL Server, Oracle, DB2, Teradata, and so forth. Analysis Services can easily deal
with retrieving relational data from various relational databases. Data source objects contain
information needed to connect to a data source such as server name, catalog or database
name, and login credentials. You establish connections to relational servers by creating a data
source for each one.
‰ Data Source Views: When working with a large operational data store, you don’t always
want to use all the tables in the database. With Data Source Views (DSVs), you can limit the
number of visible tables by including only the tables that are relevant to your analysis. DSVs
enable you to create a logical data model upon which you build your multidimensional database.
A DSV can contain tables from one or more data sources. Data sources and DSVs are
discussed in Chapter 4.

Date 10/800: thứ 6 04-10-2013

Date 9/800: Mining structure and model

Date 9/800: Mining structure and model

Date 9/800: Mining structure and model

Structures and Models As detailed in Chapter 3, SQL Server Analysis Services has two major objects that deal with data mining: mining structures and mining models. A mining structure defines the domain of a mining problem, whereas a mining model is the application of a mining algorithm to the data in a mining structure. A mining structure contains a list of structure columns that have data and content types, bindings to the data source, and some optional flags that control how the data is modeled. Additionally, a mining structure contains a list of mining models that use the columns from the structure. The definition of a mining model contains an algorithm with its associated parameters, plus a list of columns from the mining structure. Each model in a structure can use a different algorithm, or the same algorithm with different parameters, and/or a different subset of the columns in the structure. For each column in the model, you can assign how it is to be used in that model, as well as algorithm-specific modeling flags. This feature allows you to easily test different hypotheses on the same data set.

Structures and Models As detailed in Chapter 3, SQL Server Analysis Services has two major objects that deal with data mining: mining structures and mining models. A mining structure defines the domain of a mining problem, whereas a mining model is the application of a mining algorithm to the data in a mining structure. A mining structure contains a list of structure columns that have data and content types, bindings to the data source, and some optional flags that control how the data is modeled. Additionally, a mining structure contains a list of mining models that use the columns from the structure. The definition of a mining model contains an algorithm with its associated parameters, plus a list of columns from the mining structure. Each model in a structure can use a different algorithm, or the same algorithm with different parameters, and/or a different subset of the columns in the structure. For each column in the model, you can assign how it is to be used in that model, as well as algorithm-specific modeling flags. This feature allows you to easily test different hypotheses on the same data set.

Date 9/800: Mining structure and model