数据仓库与数据挖掘综述详解.ppt

“Vertical” Data Mining Generic data mining tools? —Too simple to match domain-specific, sophisticated applications Expert knowledge and business logic represent many years of work in their own fields! Data mining + business logic + domain experts A multi-dimensional view of data miners Complexity of data: Web, sequence, spatial, multimedia, … Complexity of domains: DNA, astronomy, market, telecom, … Domain-specific data mining tools Provide concrete, killer solution to specific problems Feedback to build more powerful tools Invisible Data Mining Build mining functions into daily information services Web search engine (link analysis, authoritative pages, user profiles)—adaptive web sites, etc. Improvement of query processing: history + data Making service smart and efficient Benefits from/to data mining research Data mining research has produced many scalable, efficient, novel mining solutions Applications feed new challenge problems to research Towards Intelligent Tools for Data Mining Integration paves the way to intelligent mining Smart interface brings intelligence Easy to use, understand and manipulate One picture may worth 1,000 words Visual and audio data mining Human-Centered Data Mining Towards self-tuning, self-managing, self-triggering data mining Integrated Mining: A Booster for Intelligent Mining Integration paves the way to intelligent mining Data mining integrates with DBMS, DW, WebDB, etc Integration inherits the power of up-to-date information technology: querying, MD analysis, similarity search, etc. Mining can be viewed as querying database knowledge Integration leads to standard interface/language, function/process standardization, utility, and reachability Efficiency and scalability bring intelligent mining to reality 数据挖掘与标准化进程 CRISP—DM 过程标准化（CRoss-Industry Standard Process for Data Mining） XML 与数据预处理相结合 SOAP（Simple Object Access Protocol ） 数据库与系统互操作的标准 PMML 预言模型交换标准 OLE DB For Data Mining 数据挖掘系统基于API的接口 提纲 数据仓库概念 数据仓库体系结构及组件 数据仓库设计 数据仓库技术（与数据