A brief explanation of common nouns of business intelligence

Data Warehouse
The middle of the 80 century, "the father of the Data Warehouse Mr. William H.inmon defined the concept of data warehousing in his book" Building Data Warehouses, "and then gave a more precise definition: The Data Warehouse is a topic-oriented, integrated, time-related, and relevant in business management and decision making, A collection of data that cannot be modified. Unlike other database applications, a data warehouse is more like a process for the integration, processing, and analysis of business data that is distributed throughout the enterprise. Rather than a product that can be purchased.

Data Mart
Data marts, or "small data warehouses." If the Data warehouse is built on the enterprise-level data model. The Data mart is a subset of the enterprise-class data Warehouse, which is primarily for departmental business and is only geared towards a specific topic. Data marts can mitigate bottlenecks to access the data warehouse to some extent.

Olap
The concept of online analytical processing (OLAP) was first proposed by the father of the relational database, E.f.codd, in 1993. At that time, Codd that the online transaction processing (OLTP) can not meet the end-user needs of database query analysis, SQL for the large database simple query can not meet the needs of user analysis. The user's decision analysis needs a lot of calculation to the relational database to get the result, and the result of the query can't meet the demands of the decision-makers. So Codd puts forward the concept of multidimensional database and multidimensional analysis, that is, OLAP. Codd presents OLAP's 12 guidelines to describe OLAP systems:
Benchmark 1 OLAP models must provide a multidimensional conceptual view
Guideline 2 Transparency Guidelines
Benchmark 3 access capability speculation
Guideline 4 Stable reporting capability
Benchmark 5 client/server architecture
Criterion 6-D equivalence criterion
Guideline 7 dynamic Sparse matrix processing criterion
Guideline 8 Multi-user Support competency Guidelines
Guideline 9 non-restricted cross-dimension operations
Guideline 10 Intuitive data manipulation
Guideline 11 Flexible report generation
Guideline 12 non-restricted dimension and aggregation hierarchy

ROLAP
Based on the 12 guidelines of Codd, each software development manufacturer has a different opinion, one of which is that the relational database can be used to store multidimensional data, so the star structure based on sparse matrix representation (star Schema) appears. Later, the snowflake structure was evolved. In order to distinguish from multidimensional database, OLAP based on relational database is called relational OLAP, referred to as ROLAP. Represents a product with Informix Metacube, Microsoft sql Server OLAP Services.

MOLAP
Arbor software strictly follow the definition of Codd, the establishment of a multidimensional database, to store online analysis system data, creating a multidimensional data storage precedent, and later many companies have adopted multidimensional data storage. is called Muiltdimension OLAP, referred to as MOLAP, on behalf of products have Hyperion (formerly Arbor Software) Essbase, Showcase strategy and so on.

Client OLAP
Relative to server OLAP. Some of the analysis tool manufacturers recommend that some of the data download to local, to provide users with local multidimensional analysis. Represents a product that has brio designer,business Object.

Dss
Decision Support Systems (Decision Support system), which is equivalent to data warehouse based applications. Decision support is the collection of all relevant data and information, processed and collated, to provide information for the decision management of enterprises, to provide the basis for decision makers.

Etl
The process of data extraction (Extract), Transformation (Transform), cleaning (cleansing), loading (load). It is an important part of the data Warehouse, the user extracts the required data from the data source, cleans the data, and finally loads the data into the data warehouse according to the predefined data warehouse model.

Ad hoc query
Ad hoc query, the most popular database application of a query, the use of data warehousing technology, can allow users to face the database at any time, access to the desired data.

Eis
The Executive Information System (Executive information System) refers to an application designed specifically to access a data warehouse with a simple graphical interface in order to satisfy the information query needs of leaders who cannot focus on computer technology.

Bpr
Business process re-engineering (Business process reengineering) refers to the use of data warehousing technology to identify and correct the drawbacks of enterprise business processes, a work of data warehousing, one of the important role.

BI
Business Intelligence (Business Intelligence), refers to the data Warehouse related technology and application of the generic term. Refers to the use of various intelligent technology to enhance the business competitiveness of enterprises.

Data Mining
Data mining, data mining is a decision support process, it mainly based on AI, machine learning, statistics and other technologies, highly automated analysis of the original data of the enterprise, to make inductive reasoning, mining potential models, predicting customer behavior, help the enterprise decision-makers adjust market strategy, reduce risk, Make the right decisions

Crm
Customer Relationship Management (relationship Management), the Data Warehouse is based on the database technology, but with the traditional database application has a fundamental difference between the new technology, CRM is based on the Data Warehouse technology, a novel application. However, in terms of business operation, CRM should be regarded as an old "application". For example, the hotel's management of guest information, if a guest is a regular customer of a hotel, then the hotel will naturally know that the guest's certain habits and preferences, such as whether to like the roadside, whether smoking, like the big bed, like what kind of breakfast, and so on. When guests come again, the hotel will provide the guest's favorite rooms and services without the guests ' own suggestions. This is a kind of CRM.

Meta Data
metadata, the data of Data Warehouse, refers to the data source definition, the target definition, the conversion rule and so on which produces in the Data warehouse construction process the key data related. The metadata also contains business information about the meaning of the data, all of which should be kept properly and managed well. Facilitate the development and use of the Data Warehouse.

Fact table

is refers to the multidimensional analysis contains the dimension information and the index information the physical table, may obtain by the drill slice and so on the processing way the customer needs to observe the data information the source table

Dimension table

is the relative fact table, in the multi-bit analysis, the code table used to build the dimension such as organization, time, currency, region, etc.
Drilling and fetching

Refers to the various methods used in multidimensional analysis to observe the presentation of data, including drilling and slicing.

Particle size

Refers to the degree of precision on the dimension selected when analyzing metrics, such as at year level, or month level, which is to analyze data at different granularity

Kpi

It is the key performance index that the organization uses to measure performance, and it is the important index of data Warehouse analysis and research performance Indicators,kpis.

Ods

ODS is known as the operational data store, the main function of which is to provide data (as a EDW data source) to the Data Warehouse.