Institutions of higher learning information management and information systems professional textbook series: data warehouse and data mining technology(Chinese Edition)

Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: December 2012 Pages: 274 Language: Chinese in Publisher: Tsinghua University Press institutions of higher learning of information management and information systems professional textbook series: data warehouse and data mining technology describes the data warehouse and data mining technology basic concepts. technology and application case. The universities information management and information systems professional textbook series: data warehouse and data mining technology is divided into nine chapters. including: concepts and architecture of data warehouse and data mining. data warehouse development model. etl. olap technology . business intelligence systems. data preprocessing technology. data mining. data warehouse development instance. report design and content. The universities information management and information systems professional textbook series: data warehouse and data mining technology. the case of the chapters are microsoftsqlserver2005 operating practice to explain. Through the study and practice of specific instances. so that readers master necessary knowledge points in the data warehouse and data mining. and so on so forth. The universities information management and information systems professional textbook series: data warehouse and data mining technology for undergraduate students of institutions of higher learning materials are also available enterprise information management staff. technicians and software developers to read reference. Contents: Chapter 1. data warehouse and data mining research and development status of the development of 1.1.3 data warehouse 1.1.4 1.1.2 Overview 1.1 data warehouse generation and development 1.1.1 data warehouse generation data warehouse data warehouse 1.2 Definition and basic properties of the data warehouse of the basic concepts of data warehousing 1.2.1 1.2.2 data warehouse and database difference 1.2.3 organizational structure of the data warehouse data data warehouse architecture 1.3 1.3.1 virtual data warehouse architecture 1.3. 2 separate data warehouse architecture 1.3.3 separate data mart architecture distributed data storage layer related to the concept of the data warehouse data warehouse structure 1.4 1.4.1 data source 1.4.2 1.3.4 1.4.3 olap server 1.4. data mining capabilities of object 1.5.4 Background 1.5.2 front-end tools 1.5 data mining techniques Overview 1.5.1 data mining techniques to generate the basic concepts of data mining 1.5.3 Data Mining 1.5.5 Data Mining and traditional analysis method. the difference between 1.5 1.6 .6 data warehouse and data mining relations 1.5.7 Data Mining trends data mining process the 1.6.1 fayyad process model 1.6.2 1.6.3 Other data mining process model of crisp-dm process model 1.7 commonly used data mining technology 1.8 Summary 1.9 Exercises Chapter 2. data warehouse development model 2.1 data warehouse development model Overview 2.2 2.2.1 enterprise model of the conceptual model of data warehouse 2.2.2 specification data model 2.2.3 common conceptual model 2.3 data warehouse logical model 2.3 data warehouse physical model of the design points 2.4.2 data warehouse physical model storage structure 2.4.3 2.4.1 .1 fact table model design 2.3.2 dimension table model design 2.4 data warehouse physical model physical model index build 2.4.4 2.5 data warehouse metadata model of the physical model of the optimization problem of the data warehouse 2.5.1 metadata type 2.5.2 role of metadata 2.5.3 metadata collection and maintenance of the use of the metadata 2.5.4 2.5.5 metadata management 3.1 etl concept 3.1.1 Data 2.6 data warehouse model granularity and aggregation model 2.6.1 data warehouse the granularity model 2.6.2 data model and data warehouse aggregation split 2.7 Summary 2.8 Exercises Chapter 3 etl technical understanding 3.1.2 Data extraction 3.1 3.2.1 etl systems face challenges .3 data cle.