On the powerful functions of business intelligence tools OLAP

OLAP (Online analytical Processing) is an online data access and analysis for a specific problem. Provides fast, stable, and interactive access to information (multidimensional data) in a variety of possible forms, and allows management decision makers to drill down into the data.

first, what is OLAP

OLAP (Online analytical Processing) is a kind of software technology that enables analysts, managers, or executives to gain a deeper understanding of data by rapidly, consistently, and interactively accessing information from raw data that can be truly understood by the user, and which actually reflects the characteristics of the enterprise dimension, from a variety of angles.

second, the development background of OLAP

In the 60 's, e.f.codd, the parent of the relational database, proposed a relational model that facilitated the development of online transaction processing (OLTP) (data is stored in tabular form rather than file mode). 1993, E.f.codd put forward the concept of OLAP, that OLTP can not meet the needs of the end-user database query analysis, SQL to large database simple query can not meet the requirements of end-user analysis. The decision-making analysis of the user requires a lot of computation to the relational database to get the result, and the result of the query can't meet the demand of the decision maker. Therefore, E.f.codd put forward the concept of multidimensional database and multidimensional analysis, namely OLAP.

OLTP vs. OLAP

OLTP data	OLAP data
Raw data	Exporting data
Details of the data	Comprehensive and refined data
Current Value data	Historical data
Can be updated	Not updatable, but periodically refreshed
Small amount of data processed at one time	Large amount of data processed at one time
Application-oriented, transaction-driven	Analysis-oriented, analysis-driven
For operators, support daily operations	For decision makers, support management needs

iii. Basic concepts of OLAP

1. Dimension: It is the specific angle that people observe the data, it is a kind of attribute when considering the problem, the attribute set forms a dimension (Time dimension, Geography dimension, etc.).

2. Hierarchy of dimensions: People observe that a particular angle of data (that is, a dimension) can also have various descriptive aspects (Time dimension: date, month, quarter, year) with different levels of detail.

3. Member of the dimension: A value of the dimension. Is the description of the position of the data item in a dimension. (“ a certain day of the year ” is a description of the location on the Time dimension)

4. Multidimensional arrays: A combination representation of the variables. A multidimensional array can be represented as: (Dimension 1, Dimension 2,…, dimension n, variable). (Time, region, product, sales)

5. Data unit (cell): The value of a multidimensional array.

iv. features of OLAP

(1) Fast: Users have high requirements for the rapid response ability of OLAP. The system should be able to respond to most of the user's analysis requirements within 5 seconds.

(2) can be analyzed: OLAP systems should be able to handle any logic analysis and statistical analysis related to the application.

(3) Multidimensional nature: Multidimensional is the key attribute of OLAP. The system must provide multidimensional views and analysis of the data, including full support for hierarchical and multi-layered dimensions.

(4) Information: Regardless of the amount of data, no matter where the data is stored, OLAP systems should be able to timely access to information, and management of large-capacity information.

Five, OLAP multidimensional data structure1. Super cubic structure (hypercube)

A hypercube structure refers to a three-dimensional or more dimension to describe an object, each of which is perpendicular to each other. The measured values of the data occur at the intersection of the dimensions, and each part of the data space has the same dimension attributes. (Shrinks the hypercube structure.) The data density of this structure is greater, the dimensionality of the data is less, and additional analytic dimensions can be added.

2. Multi-cubic structure (multicube)

The hypercube structure becomes a sub-cubic structure. The segmentation of dimensions for a particular application is highly flexible and improves the efficiency of data (especially sparse data) analysis.

Six, OLAP multidimensional data analysis1. Slicing and dicing (Slice and Dice)

In the multidimensional data structure, by two-dimensional slicing, three-dimensional cutting, you can get the required data. such as in the “ city, product, time ” three-dimensional cube in the cut and slicing, can get the city, the sales of products.

2. Drilling (Drill)

Drillthrough includes drill-down (drill-down) and drill-up (drill-up)/roll-up (roll-up) operations, and the depth of the drill corresponds to the level of the dimension.

3. Rotation (Rotate)/hinge (pivot)

Data from different perspectives can be obtained by rotating.

vii. Classification of OLAP products

There are many products on the market about OLAP applications, to the real needs of users, how to select the appropriate for their own OLAP products appear dizzying, the following to recommend several users to obtain a number of user-praised OLAP products, first of all we look at now including the future of OLAP can be used in the field of what:

1. Market and Sales Analysis (Marketing
2. E-Commerce analysis (Clickstream analyst)
3. Marketing based on historical data (database marketing)
4. Budget (budgeting)
5. Financial reporting and integration (Financial reporting and consolidation)
6. Management report (Management reporting)
7. Interest rate analysis (profitability)
8. Quality Analysis (Quality)
9, OLAP standard APB-1 (aqt-analytical Query time as a statistical indicator)

OLAP Web-based applications
static method static HTML report
Dynamic methods generate reports dynamically through HTML templates and meta-data
Improved method using Java or ActiveX

Related Products:

Sharpshooter olap— for multidimensional data analysis and graphical display of data

Features:

• With sharpshooter OLAP embedded in the application, users can view any amount of data in any way, at any time, and perform interactive analysis of any type and quality

• Sharpshooter provides flexible charting options and creates complex chart structures, as well as many chart types to choose from, such as pie charts, bar charts, stacked bar charts, style curves, and more. Combined use of components allows you to quickly turn your data into a visual work

• OLAP component packages can help you manage and analyze your data

Pivot Table & charts— for online viewing, analysis, and management of multidimensional data

Features:

• Quickly display complex data from OLAP cube blocks, SQL databases, or static CSF files as compact, broad-based visualization reports, similar to Excel PivotTables and charts

• The PivotTable widget helps you create interactive PivotTable and chart reports for Web, Flex, and mobile applications. Pivot Widget provides an extremely intuitive end-user experience for your customers while creating and analyzing reports

• It is easy for users to change the appearance of the report and examine the data from different perspectives. You don't have to anticipate all the possible reporting scenarios to meet the potential needs of your end users

• Supports multiple types: histogram, cylinder, line, scatter, stacked and pie charts

• Users can set the layout of the report in just a minute-determine which dimensions should be set in the row, column, or filter area

Radarcube ASP & Silverlight — visualize OLAP data in tabular and graphical format

Features:

• Supports any type of data source, such as databases, files, and so on. Multidimensional data sources that support SQL Server Analysis Services cubes

• Create calculated measures for different types (measure values based on row-table, custom aggregate measures, and measures based on other cube cells)

• Modifications to OLAP chart elements, including colors, forms, and dimensions that depend on the values of different measurements. Non-associative and continuous color modification

Viii. Development and prospect of OLAP

Due to the large increase in computer capabilities and the efforts of various manufacturers, OLAP has overcome many of the original technical difficulties can not be achieved. Due to the explosion of data and the new functional requirements of customers, new OLAP technologies are emerging both on the server and on the client side. As follows:

Object-oriented online analytical processing
O3lap (object-oriented OLAP)
On-Line Analytical processing of object relationships
Orolap (Object relational OLAP)
Distributed online Analytical Processing
Dolap (Distributed OLAP)
Temporal online Analytical processing
Tolap (temporal OLAP)

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On the powerful functions of business intelligence tools OLAP