Basic concepts of Data Warehouse

1. The concept of Data Warehouse began in the 1980s S and first appeared in the book William H. Inmon, known as "the father of Data Warehouse. With
Profound recognition and continuous improvement in system research, management, and maintenance. After summing up, enriching, and integrating the experience of multi-row enterprise information, we provide a more precise definition for the data warehouse, that is, "data warehouses are subject-oriented, integrated, time-related, and unchangeable data sets in enterprise management and decision-making ". Data Warehouses do not have a strict theoretical basis for data, nor have mature basic models. They prefer engineering and have a strong engineering nature. Generally, data extraction, storage and management, and data performance are divided into three basic aspects based on their key technical components.
The key and requirement of a data warehouse is that it can accurately, securely, and reliably retrieve data from the database, process and convert the data into regular information, and then analyze and use the data for management personnel. Data Warehouse
It is mainly used in the decision support system. Its main purpose is to "extract" Information and expand it for Processing decisions based on data warehouses.
Policy Support System (DSS) applications.
2 Data Warehouse-based decision support system Data warehouse-based decision support system (DSS) consists of three components: Data warehousing and OLAP, on-Line Analytical Pro-cessing), Data Mining technology (Data Mining ).
OLAP (On-Analytical Pro-cessing) enables analysts, managers, or executors to convert data from raw data from multiple perspectives.
This is a type of software technology that allows you to quickly, consistently, and interactively access the information you understand, and truly reflect the information you provide for features, so as to gain a deeper understanding of data. OLAP aims to meet the query and report requirements of decision support or multi-dimensional environments. Data Warehouses focus on storage and management of decision-making-oriented data, while OLAP focuses on data analysis of data warehouses.
It is converted into auxiliary decision information. A major feature of ola p is multi-dimensional data analysis, which forms a combination and complementary relationship with the multi-dimensional data organization of the data warehouse. Ask this, use
The combination of OLAP technology and data warehouse can better solve the problem that traditional decision support systems need to process a large amount of data and perform a large amount of numerical computing.
OLAP multi-dimensional data analysis provides in-depth analysis of the data provided by the database by cutting, drilling, and rotating the dimensions of multi-dimensional data, and provides decision-making support for decision makers. Multiple
The dimension structure is the pillar of decision support and the core of OLAP.
Data Mining is a massive, incomplete, and noisy process. Fuzzy and random data is used to extract hidden, unknown, but potentially useful information.
Information and knowledge process.
Data mining can be seen as a data search process. It does not have to make assumptions or ask questions in advance, but still can find unexpected but interesting information, this information indicates the relationship and pattern of data elements. It can explore the pattern of a data key and find the most valuable information and knowledge ). Guides business behavior or assists in scientific research. The study targets large and ultra-large data sets.