数据仓库与数据挖掘

本文介绍了数据仓库与数据挖掘的基本概念、特点和体系结构,讨论了数据挖掘的技术算法及数据仓库与数据挖掘的应用领域。     

数据仓库与数据挖掘浅析

本文介绍了数据仓库的概念，阐述了建立数据仓库的步骤，以及实施过程中需要注意的问题。接着本文又对数据挖掘进行了分析，分别就数据挖掘的任务、挖掘流程进行了探讨，并对数据挖掘的的发展进行了展望。     

数据仓库与数据挖掘技术

数据挖掘是一新兴的技术,近年对其研究正在蓬勃开展。本文阐述了数据仓库及数据挖掘的相关概念,做了相应的分析,同时共同探讨了两者共同发展的关系,并对数据仓库与挖掘技术结合应用的发展做了展望。     

数据仓库与数据挖掘浅析

本文介绍了数据仓库的概念,阐述了建立数据仓库的步骤,以及实施过程中需要注意的问题。接着本文又对数据挖掘进行了分析,分别就数据挖掘的任务、挖掘流程进行了探讨,并对数据挖掘的的发展进行了展望。     

数据挖掘与数据仓库分析

现代经济发展中,信息的处理是问题解决、分析和决策的重要依据.数据挖掘技术和数据仓库技术是信息处理的核心技术.本文分别分析了数据挖掘技术和数据仓库技术的理论基础和技术应用,阐述信息处理技术中数据挖掘和数据仓库的应用和前景.     

数据仓库与数据挖掘技术

数据挖掘是一新兴的技术,近年对其研究正在蓬勃开展.本文阐述了数据仓库及数据挖掘的相关概念,做了相应的分析,同时共同探讨了两者共同发展的关系,并对数据仓库与挖掘技术结合应用的发展做了展望.     

数据仓库与数据挖掘研究进展

随着数据库应用从以事务处理为中心逐渐向以分析处理为中心转化,以数据仓库、联机分析和数据挖掘为中心的基于数据库的分析技术成为近几年来数据库界研究的热点。经过近十年的研究与应用。已经产生了很多的研究成果,建立了较扎实的理论基础,目前正在向与应用领域紧密结合(如流数据)。对现有技术进一步改造方面发展。本文对近两年这几个方向上的研究成果进行了总结。     

数据挖掘与数据仓库的关联

本文简述了数据挖掘和数据仓库的相关知识,探讨了数据挖掘和数据仓库之间的紧密关系。     

超市数据仓库与数据挖掘的研究

传统的数据库管理信息系统不能够很好地利用、分析数据库中积累的大量数据,数据仓库与数据挖掘技术很好地解决了这一问题.通过分析超市业务数据库,用星型架构的方式建模,构造出一个数据仓库的逻辑模型;然后从超市业务数据库中抽取数据,经过转换等处理,把有价值的数据加载到数据仓库中,完成数据仓库的构建,并论述客户分片的数据挖掘过程.     

基于数据仓库的Web日志挖掘技术研究

Web日志挖掘是目前Web挖掘研究的一个重点.针对Web日志挖掘中存在的问题,给出了基于数据仓库技术的Web日志挖掘方案,就数据预处理、数据立方体设计及数据挖掘技术的应用进行了较为深入的探讨.并以一个Web站点日志为例,详细阐述了Web日志数据预处理、Web日志立方体设计以及数据挖掘算法的实现过程,并实现了一个Web日志多维数据集,能够有效解决Web日志分析中的难题.     

Data warehousing and data mining techniques for intrusion detection systems

This paper describes data mining and data warehousing techniques that can improve the performance and usability of Intrusion Detection Systems (IDS). Current IDS do not provide support for historical data analysis and data summarization. This paper presents techniques to model network traffic and alerts using a multi-dimensional data model and star schemas. This data model was used to perform network security analysis and detect denial of service attacks. Our data model can also be used to handle heterogeneous data sources (e.g. firewall logs, system calls, net-flow data) and enable up to two orders of magnitude faster query response times for analysts as compared to the current state of the art. We have used our techniques to implement a prototype system that is being successfully used at Army Research Labs. Our system has helped the security analyst in detecting intrusions and in historical data analysis for generating reports on trend analysis. Recommended by: Ashfaq Khokhar     

Using object deputy model to prepare data for data warehousing

Providing integrated access to multiple, distributed, heterogeneous databases and other information sources has become one of the leading issues in database research and the industry. One of the most effective approaches is to extract and integrate information of interest from each source in advance and store them in a centralized repository (known as a data warehouse). When a query is posed, it is evaluated directly at the warehouse without accessing the original information sources. One of the techniques that this approach uses to improve the efficiency of query processing is materialized view(s). Essentially, materialized views are used for data warehouses, and various methods for relational databases have been developed. In this paper, we first discuss an object deputy approach to realize materialized object views for data warehouses which can also incorporate object-oriented databases. A framework has been developed using Smalltalk to prepare data for data warehousing, in which an object deputy model and database connecting tools have been implemented. The object deputy model can provide an easy-to-use way to resolve inconsistency and conflicts while preparing data for data warehousing, as evidenced by our empirical study.     

Perceived influences on implementing data warehousing

This study surveyed data warehousing implementation project participants to determine what aspects they perceived should contribute to the implementation process. The respondents included: functional managers/staff, IS managers/staff, and consultants. The study identified eight significant factors that participants perceived should impact data warehouse implementation.     

Healthcare data warehousing and quality assurance

Healthcare data warehousing presents unique challenges. The industry is rife with often incompatible medical standards and coding schemes that require careful translation. Healthcare data comes from many sources and is delivered in many forms, including published books, individual spreadsheets, and several tape or data formats. Results derived from a healthcare data warehouse must be delivered in accessible form to diverse stakeholders, including healthcare regulators, physicians, hospital administrators, consumers, community activists, and members of the popular press. The industry's widely decentralized and largely autonomous data collection efforts make data quality a significant challenge. Finally, the sensitivity of healthcare data makes privacy and security issues paramount. Healthcare data warehousing will make rigorous, quantitative information available to healthcare decision makers. The authors describe a fully functional healthcare data warehouse used to produce several reports for communities throughout Florida. Building on this work, they're actively pursuing a research agenda to enhance technical data warehousing capabilities while investigating innovative community and clinical healthcare applications     

数据挖掘技术在高校教学与管理中的应用

针对传统的求总分统计成绩方法的不足,提出了一种基于K-Means算法的成绩聚类分析方法.该方法根据成绩分布情况选取固定的初始聚类中心,改进了K-Means算法随机选取初始聚类中心导致聚类不稳定的不足,在聚类后通过聚类内差异与聚类间差异的比值来衡量聚类的质量.通过一个实例说明了该方法在分析学生成绩数据中的应用,实验结果表明,聚类方法比传统的求总分方法更合理、更科学,聚类结果蕴含更多有用的信息,而且改进后的聚类方法降低了随机选取初始聚类中心所产生的结果的不稳定性,聚类效果较好.     

大数据时代数据挖掘与分析课程教学改革探究

从理论知识、编程实践、工程实战三个方面对数据挖掘与分析课程进行教学改革.课程采用Python编程语言,围绕贵州省经济文化特色及大数据发展战略,充分运用大数据、数据挖掘、知识图谱等新兴技术来分析贵州省各领域现状,挖掘其内在价值.近三年的教学实践证明了该教学改革的创新性和有效性,培养了学生的大数据思维,为贵州省聚焦大数据战...     

OLAP query reformulation in peer-to-peer data warehousing

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities, but traditional business intelligence systems do not provide support to this end. To fill this gap, in this paper we envision a peer-to-peer data warehousing architecture based on a network of heterogeneous peers, each exposing query answering functionalities aimed at sharing business information. To enhance the decision making process, an OLAP query expressed on a peer needs to be properly reformulated on the local multidimensional schemata of the other peers. To this end, we present a language for the definition of mappings between the multidimensional schemata of peers and we introduce a query reformulation framework that relies on the translation of mappings, queries, and multidimensional schemata onto the relational level. Then, we formalize a query reformulation algorithm and prove two properties: correctness and closure, that are essential in a peer-to-peer setting. Finally, we discuss the main implementation issues related to the reformulation setting proposed, with specific reference to the case in which the local multidimensional engines hosted by peers use the standard MDX language.     

OLAP数据挖掘模型的应用研究

OLAP数据挖掘模型结合了微软分析服务可以访问的数据挖掘和联机分析处理两种决策机制,其创建可以借助挖掘模型向导或利用决策支持对象编程实现。采用微软决策树算法的OLAP数据挖掘模型可根据决策树、虚拟立方体和维来分析数据,并预测数据的某些特性,以帮助用户决策。     

基于数据仓库的JMS中数据管理方法的研究

在分析JMS数据管理的目标的基础上,重点研究了系统数据和用户数据的管理模型.对系统数据,重点研究了集群配置数据和日志数据的管理方法,提出使用OGSA的分布式消息结构来管理JMS中的分布式日志.对用户数据,研究了作业和作业网络及其实例的数据管理以及用户数据传输方法,提出了使用数据仓库来分析作业执行历史,设计了JMS的数据仓库模式,该模式能满足用户的多种查询和分析要求.有效地解决了JMS中的数据存储、查询、分析和共享等问题.