Data Warehouse And Data Mining Applications In The Securities Industry

A data warehouse is a subjected-oriented, integrated, time-variant, and nonvolatile collection of data in support of management's decision making process. It is based in traditional business database, by withdrawing the large quantity business database, to form data warehouse data. The data warehouse can support for the decision-making of the decision support system and handle for the other information service system. The data mining is also called the knowledge discovery in database, it discovers from large quantity of data and find authentic, novel and effective model that can be comprehended by people. The data mining finds useful information from the data of large quantity to support for reasonable decision-making. The data warehouse, online analysis and data mining have obtained the extensive application abroad, but in China the application just starts. The stock industries use the information technique early in China and establish perfect OLTP. The application of many years also makes stock companies backup flood of data, among them implicit worthy information in large quantity. How to make use of these data, discover information on the deep level and serve for the management's decision making, becomes the urgent matter of the moment of the stock company.We look up to the files to understood the present condition of local parts of industries, such as the finance, telecommunication, retail trade, in data warehouse and data mining. We discover that majority of f these enterprises have built perfect IT infrastructures, but because of some reasons, such as funds, profession technical personnel, they make few development in data analyzing. The paper analyses the explanatory problem in this kind of present condition and the perplexity of the business enterprise, aiming at these problems on the applications. We introduce the knowledge about data warehouse and data mining in detail, such as the definition and tools of data warehouse and data mining. We take the application of data warehouse and data mining in stock as example. By investigating and anglicizing in it, we canlearn the process of establishing data warehouse and applying data mining. The paper includes the requirement analysis (data environment analysis, structure analysis of database and subject analysis of system), the data warehouse application system construction design, data model design; transferring data in data warehouse; data loading and creation of multi-dimensional data sets. Finally we use data-mining tools to make OLAP analysis and association rules analysis on stock data warehouse to find ways to comprehend outcome of analysis. The enterprises may have a direct and thorough view on application of data warehouse and data mining in business and how to realize it in their own business.The paper introduces the latest development and basic principle of date warehouse and data-mining, discussing the technique in data warehouse application meaning in stock company. The paper discuss the theory and ways of establishing data warehouse based on business system foundation in stock company, providing the basic theories for the data warehouse application in stock company and practical methods. Required with the business and data characteristics of the stock company, the paper puts forward the total frame of the stock company data warehouse system. Around the stock sales subject, we set up a case about design of data warehouse to find how to design target data warehouse in stock company. The paper discusses ETL techniques which transfer the data to data warehouse., and complete the transformation by the help of MS SQL Server to built multi-dimensional data sets about the stock sales.The paper is divided into totally six chapters. Chapter 1 introduces the data background and the meaning of data mining; Chapter 2 explains the concerning basic theories upon the data warehouse and data mining; Chapter 3 analysis the present progress made by researchers in and abroad; Chapter 4 expatiate meaning of establishing data ware house in stock industry and process in detail; Chapter 5 expatiate that we proceed the OLAP analysis and association rules analysis on the basis of data warehouse; Chapter 6 is the summary of total paper.The innovation of paper is that we construct two stock sequence rule model with time constraint: the stock's sequence rule model of one dimension with certain timesegment (represented by w) constraint and the stock's sequence rule model of two dimensions with W and time-interval (represented by INT) constraint.The innovation of paper is that that we construct two stock sequence rule models with time constraint: the stock' sequence rule model of one dimension with certain time-segment (represented by constraint) and the stock1 sequence rule model of two dimensions with W and time-interval (represented by INT) constraints. This means that if the closing price of stock A is going up to X% in time-segment W, then those of stock B and will also rise (or descent) in Y% probability in time-segment just after INT time-segments. By this we can find some useful model that can hardly be found with traditional statistical methods.In chapter 5 we make a substantial evidence to verify the possibility of model presented in paper. In the stock analysis realm, this model will help the investor make more reasonable and more complete analysis, thereby improving the quality of the decision-making. This model also can be used in other realms such as bank, telecommunication industry etc.