基于数据仓库的OLAP技术在家电零售企业中的应用研究

数据仓库及OLAP技术是当前数据库应用领域的热点,国内学者以往的研究主要侧重于OLAP技术中的数据模型等问题的研究,本文则侧重于应用相应OLAP技术与数据模型进行商业数据分析,这对该领域的用户更具有参考价值。我们在介绍OLAP与MDX的基础上,通过某大型家电零售公司为背景,详细探讨了基于数据仓库的多维数据分析技术在实践中的应用。     

基于GIS的统计数据可视化分析

随着计算机技术的不断发展,GIS技术应用到地理空间数据分析领域,提高抵抗空间数据的分析水平。本文介绍地理空间数据可视化的内涵,结合统计数据和GIS的特点,提出基于GIS的统计数据可视化流程和体系。同时,以国家社会经济统计地理信息系统为实例,研究基于GIS的统计数据可视化的应用问题,证明可视化体系和功能流程模式具有十分重要的推广价值。     

OLAP和DM在MRPII/ERP中的应用研究

本文针对传统MRPII/ERP系统在决策方面的不足,提出把联机分析处理和数据挖掘技术应用到传统MR-PII/ERP中去,以数据仓库作为企业底层的数据源,再配合以各种联机分析处理和数据挖掘技术,并结合MRPII/ERP的管理思想,正好做到取联机分析处理和数据挖掘在决策支持方面之长,补传统MRPII/ERP之短.同时,提出了智能制造资源计划(IMRPII)和智能企业资源计划(IERP)的系统模式,即IMRPII/IERP=MRPII/ERP+DW+OLAP+DM.并且针对MRPII中的预测功能,设计出了智能预测的方法.利用数据挖掘技术,动态地设置预测个体的预测权值,提高了定性预测的准确性和科学性.     

审计软件算法研究

本文采用将OLAP技术与数据挖掘技术视为两个不相交领域的划分方法,认为数据挖掘技术是在OLAP技术基础上更进一步的发展,但这并不意味着在任意领域数据挖掘技术总是比OLAP更为有效;在特定应用场合,例如在目前技术条件下的审计领域,OLAP的技术特点反而更适合审计业务要求,这正是区分OLAP技术与数据挖掘技术的意义所在;在此基础上,进一步根据数据仓库存储方式对几种不同的OLAP模式进行细分;最后详细分析了基于MDX查询的OLAP算法原理,并首次给出了一种完整的基于MDX查询的OLAP算法的代码实现。     

数据挖掘技术在水利工程管理中的应用研究

因能从海量数据中发现潜在的、有价值的知识,数据挖掘成为人们非常感兴趣的热点技术。水利工程管理面临的是海量的非空间数据和空间数据,对这些数据的挖掘,能够提炼出有价值的知识,从而提高水利工程管理的科学化水平和决策水平。在水利工程管理中利用空间数据挖掘技术。需要解决数据仓库建设、数据挖掘与GIS集成和数据挖掘系统模型等3个方面的关键问题。数据仓库是建设水利工程数据挖掘系统的基础。数据挖掘与GIS集成的方式有嵌入式、松散耦合式、紧密耦合式3种。紧密耦合式是两者集成的最好方式。水利工程系统的数据挖掘适合采用基于OLAP和0LAM的探查性数据挖掘模型。     

数据仓库技术在高校大类招生专业选择分析中的应用

本文研究数据仓库技术在高校大类招生环境下,学生自主选择专业行为特征分析中的应用,为高校大类招生相关政策的制定提供理论支持及技术手段.首先介绍了数据仓库星形模型分析方法,结合专业选择分析相关需求描述数据仓库建立的一般步骤.随后以北京科技大学经济管理学院2005级学生基本信息及学科成绩为例,重点阐述学生专业选择数据仓库逻辑模型设计.通过确立学生基本影响因素分析和学生成绩分析两大主题,充分分析学生基本属性、学生成绩等对学生选择专业的影响,并给出OLAP分析结论.     

ERP嵌入OLAP技术的财务数据查询方法探讨

OLAP技术是多维快速查询技术,被广泛用于经济管理.可用两种方法在ERP中嵌入OLAP技术,把各功能数据转化为财务专业数据,进行编辑分析、查询,及时有效地多方位输出财务数据,对改变现阶段财务数据利用滞后性,实现事前准确预算、事中及时控制,提高动态管理水平起重要作用.     

基于OLAP的高校财务决策研究

高校财务信息化建设积累了丰富的业务数据,如何利用历史数据进行高校财务决策,是管理者关心的问题。本文通过财务决策主题,设计出多维立方体,并对其执行联机分析处理（OLAP）;利用决策联机分析处理平台进行分析,并给出了相应的分析结果。决策OLAP分析的应用使高校财务管理系统获得更高的实用价值。     

浅谈水轮机主轴密封的几种形式

数字城市的本质就是海量城市空间数据与三维城市地理信息系统、时序城市地理信息系统的融合.对于通过动态获取的大量城市影像信息,经过高效的数宁加工和处理,提取三维城市地物的位置信息、几何信息、特征信息、分类信息和属性信息,加上一定的整合手段,就可快速高精度得到城市规划中三种基本图件--大比例尺数字地形图、城市现状用地分类图、城市现状建筑分类图.从而方便地服务于城市总体规划工作中方案的形成,方案影响因素分析和输出等全过程.     

互联网商品评论情感分析研究综述

互联网评论情感分析是Web信息挖掘的一个新兴领域,近年来受到计算机科学、经济学、管理学等相关学科的广泛关注.目前,情感分析领域的研究主要集中在主观性内容识别、褒贬情感分类以及在线评论的经济价值挖掘等几个方面,大部分研究借鉴文本挖掘、信息检索、机器学习、自然语言处理、统计学等方面的技术和方法,也提出了一些针对评论情感分析的特定方法.对在线评论情感分析领域的研究现状与进展动态进行归纳和分析,重点论述现有研究采用的主要方法和关键技术,以及研究中存在的问题,最后提出了未来的研究方向.     

辅助决策系统在企业中的应用

辅助决策可以向企业的各级决策者提供及时准确的信息,帮助他们做出对企业更有利的决策,是对原有信息系统的再次升华。     

商业智能及其在电子商务交易中的应用研究

商业智能（Business Intelligence,BI）是将数据仓库、数据挖掘、OLAP等技术结合起来,对商业信息进行搜索、分析,从而得到支持企业各级决策知识的一种新技术方法。如今电子商务发展迅速,无处不影响着企业的活动和发展。本文将研究商业智能在电子商务交易中的应用,使电子商务进一步为企业创造利润和价值。     

供水调度中实现决策支持的方法研究

针对供水调度系统在决策支持方面的不足,本文提出利用SQL Server Analysis Services在OLAP及数据挖掘方面的技术,建立数据仓库、OLAP和数据挖掘三者相结合的决策支持系统的方案,讨论了基于Analysis Ser-vices实现供水调度决策支持系统的技术.     

A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.     

Spatial Data Usage,Spatial Thinking and Spatial Knowledge Generation: The Case of Planning Practitioners in Arequipa,Peru

Spatial thinking and spatial knowledge generation in decision-making are still not mature fields of study in planning research, despite these being crucial elements in addressing the issues of the twenty-first-century city. This article contributes to their understanding by exploring their interrelationships with spatial data usage. Through storylines, it analyzes the arguments that planning practitioners offer in support of infrastructure-led decisions in Arequipa (Peru), before and after spatial data usage. The article concludes that spatial data usage improve spatial thinking to different extents, yet suggests aligning spatial data generation and the inclusion of GIS-based spatial analyses with the spatial knowledge needed by routine planning practice.     

中国省域企业家精神的空间溢出效应研究

企业家精神的发展受到空间因素的影响,但之前大部分区域企业家精神影响因素实证研究都忽略了空间相关性,导致实证结果出现偏差。本文首先使用探索性空间数据分析工具研究了2000-2009年间中国各省域企业家精神的空间分布格局与特征,进一步从地理特征和社会经济特征两个方面建立空间权重矩阵,并运用空间面板数据模型考察了中国省域企业家精神的空间溢出效应。结果表明存在着全域范围的正的空间自相关性,并且局域相关显示出其空间集聚特征;中国省域企业家精神存在较强的空间溢出效应,相邻省份企业家创业活动对本省企业家创业活动有正向影响。基于模型分析结论,提出了引导创业资源空间合理流动、促进省域企业家精神发展等政策建议。     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

An Integrated Framework for Environmental Multi‐Impact Spatial Risk Analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk‐mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi‐attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood‐prone area in northern Italy.