To explain their relationship, you have to talk about business intelligence. From a technical point of view, the process of business intelligence is that the company's decision makers are based on the data warehouse in the enterprise. The data mining tools, data mining tools, and the professional knowledge of decision planners, are useful information and knowledge from the data. Help enterprises get profits.
Data warehouse is a data collection that is used to better support business or organization, which has a topic, integrated, relatively stable, continuously changing four characteristics, and puts data warehouse and traditional transactional. The database area is separated. Key Technologies of Data Warehouse include data extraction, cleaning, conversion, loading, and maintenance techniques.
Online Analysis Processing (OLAP) is a complicated analysis technology based on massive data. It supports management decision makers at all levels from different angles, quickly and flexibly perform complex queries and multi-dimensional analysis processing in data warehouses, and can present queries and analysis results to decision makers in an intuitive and easy-to-understand form. The logical data model used by OLAP is a multi-dimensional data model. Common OLAP multi-dimensional analysis operations have rolls, down drills, slices, cut, rotation, etc. The multi-dimensional data model is physically implemented, there are three ways: ROLAP structure, MOLAP structure and HOLAP structure. Where ROLAP is an OLAP implementation based on the relational database, MOLAP is an OLAP implementation based on multi-dimensional data organizations, and HOLAP is an OLAP implementation based on the mixed data organization.
Data mining is from massive data, and the process of information and knowledge that is implied therein, but may be useful, but may be useful. Data mining data has a variety of sources, including data warehouses, databases, or other data sources. All data need to be selected again, the specific option is related to the task. The results of excavation need to be evaluated to eventually become useful information, according to the evaluation results, the data may need to feed back to different stages and re-analyze calculations. Common methods for data mining include association analysis, classification, and prediction, clustering, detection of group points, trends, and evolution analysis. It can be said: online analysis processing and data mining are value-added techniques above the data warehouse.
In theoretical research, the researchers of OLAP technology mainly come from database boundaries, focus on CUBE compression and calculation, the selection and maintenance, multi-dimensional data index and multi-dimensional query processing, so that secondary data can be provided in mass data. Analysis request response time. The researchers of data mining techniques come from artificial intelligence, statistics, database boundaries, and their research mainly focused on various mining algorithms and evaluation methods, study scalable data mining methods, based on constraint mining methods, excavation of complex data types.
Online Analysis Processing and Data Mining Although data value-added techniques for obtaining two different targets on the data warehouse, if these two technologies can be integrated to a certain extent, the analysis operation is intelligent, making the mining operation target, thus comprehensively improve Practical value of business intelligence technology. That is: On the one hand, online analysis technology can provide expected excavation objects and targets for data mining to avoid blindness of excavation. On the other hand, data mining technology can make online analysis to process intelligence, reduce the complicity of analytical personnel, and reduce the burden on the analyst. For example, when the analyst finds the outbound point data in hand-analyzing operation, it can be targeted to use data mining technology to find the cause of data mining technology, and find out the malicious violation or discover new demand points. As another example, in the data analysis process, by tracking the operation of the analyst, the operation and data of his may be interested in predicting the operation and data of the analysis operation, thereby increasing the response time of the analysis operation.
It can therefore be said that the integration and complement of online analysis processing technology and data mining technology based on the data warehouse will be the future direction of business intelligence technology.
Details of the technical and application of business intelligence, see: Chen Hong, a Questional School of Renmin University of China, published in Caidi.com
Business intelligence: Det the profit from data
