面向对象技术在构建数据仓库中的应用研究

为了利用数据仓库技术实现对经济指标数据的有效管理,提出了基于面向对象技术构建数据仓库的一种解决方案。在构建过程中,根据经济指标数据管理需求和数据仓库特点,确定了系统所面对的各种对象以及它们之间的关系,设计了数据仓库结构模型,应用Visual C .NET实现了数据仓库管理系统,并通过ADO(Active Data Object)和OLE(Object Link Embedded)等面向对象技术完成了数据仓库访问和异构系统数据集成,从而使经济指标数据仓库(EIDW:Economic Indicators Data Warehouse)可作为后台数据环境应用于经济动态分析、预测和监测的决策支持系统中。     

我国B2C电子商务企业发展现状及营销策略建议

电子商务随着互联网的普及而逐渐为中国居民所认识,其中网络购物作为一种新兴的购物方式已开始为中国居民所接受.文章通过对相关文献的研究及个案分析,在对我国B2C电子商务的现状进行考察的基础上,提出了我国B2C企业在发展中应通过合理定价,提升用户购物体验;积极改善售后和客服;细分市场,积极开拓垂直类市场;深挖平台价值,丰富收入来源等建议和方法来开拓市场和应对竞争.     

独立学院大学生创业观的塑造

针对独立学院创业教育的现状、优势和存在的问题,提出在大学生创业观上必须正确处理好自身条件与社会需求、个人就业观与家庭期望、学校创业教育与个人专业学习三大关系。     

基于粒计算的商业数据流概念漂移特征选择

商业数据流具有动态性、漂移性等特性,概念漂移特征选择是数据流挖掘的重要工作之一.本文从数据流的特点和概念漂移特性出发,提出了数据流的概念形式化分析流程和基于粒计算构建数据流的概念形式化描述模型;商业数据流的概念漂移实际上取决于其概念外延的变化,文中使用包括外延偶合度和内涵偶合度在内的概念偶合度来描述概念间的相似性;经过粒化的数据流由概念格来表示,进而通过概念格对的松弛偶合度来分析数据流特征;结合概念偶合分析和数据流变化特征,阐述了一种基于数据流概念格对的松弛匹配偶合度算法,并据此分析概念格对来选择数据流的漂移特征.通过实例验证、评价了特征选择,证明其有效性.     

政府引领全民创业对策研究

通过文献资料、总结归纳等研究方法,提出了地方政府在全民创业问题上的组织引领、政策引领、服务引领、观念引领、技能引领、环境引领六项对策。     

公务卡实际应用问题对策研究

公务卡报销制度是现代信息化的标志,是电子政务化的必然结果,近几年应用过程中出现的诸多问题影响公务卡的发展。因此,针对目前公务卡应用中存在的问题进行了详细剖析,并从扩大宣传、改善用卡环境,完善公务卡银行服务体系,推动公务卡制度发展等方面提出了一些应对措施。     

商业模式创新分析——基于外部利益相关者视角

商业模式构成企业逻辑框架,同时也动态影响企业的整个运营流程,外部利益相关者作为情境变量,对商业模式价值的形成与创新有重要的影响作用。本研究将企业看作整体,通过分析政府机构、公众&社区、竞争性企业、互补性企业、供应商、消费者对企业的价值导向作用,并结合企业特征构建新的分析框架,从不同维度视角探索各类型企业在商业模式创新与外部利益相关者之间的作用关键点,为相关研究提供新的基础性研究框架。     

Land potential appraisal for urban land reserve based on GIS：

With the rapid urbanization, land banking has become an important means for rational land use and land configuration optimizing. Rational urban land reserve and supply plan are keys for an urban land banking. GIS has been used to model urban growth, growth at the ruralurban fringe specifically. This paper identifies that the urban land banking potential can be evaluated based on RS and GIS technology. 10 indicators were chosen in the integrated index system. As a case of Metropolitan area in Chongqing, urban land banking potential was evaluated based on RS and GIS technology. With GIS, two steps can help to finish potential analysis of land banking. One is goal driven process, such as the process of planning and definition; the other step is datadriven process, such as the process of Manipulating. The results are used to establish the current land banking plan.      

Enhancing survey‐based investment forecasts

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.     

Forecasting key US macroeconomic variables with a factor‐augmented Qual VAR

In this paper, we first extract factors from a monthly dataset of 130 macroeconomic and financial variables. These extracted factors are then used to construct a factor‐augmented qualitative vector autoregressive (FA‐Qual VAR) model to forecast industrial production growth, inflation, the Federal funds rate, and the term spread based on a pseudo out‐of‐sample recursive forecasting exercise over an out‐of‐sample period of 1980:1 to 2014:12, using an in‐sample period of 1960:1 to 1979:12. Short‐, medium‐, and long‐run horizons of 1, 6, 12, and 24 months ahead are considered. The forecast from the FA‐Qual VAR is compared with that of a standard VAR model, a Qual VAR model, and a factor‐augmented VAR (FAVAR). In general, we observe that the FA‐Qual VAR tends to perform significantly better than the VAR, Qual VAR and FAVAR (barring some exceptions relative to the latter). In addition, we find that the Qual VARs are also well equipped in forecasting probability of recessions when compared to probit models.