公路货运物流管理信息系统的开发与研究

在分析了公路货运物流管理系统需求的基础上,探讨了系统的功能模块划分、数据库设计方法。采用Powerbuilder开发语言和SQL Server数据库技术以及虚拟专用网络(VPN)连接技术,设计和实现了现代物流管理系统的数据库结构和应用程序及VPN网络运行环境。     

Web混合模式下面向订单装配的生产管理系统

本文介绍了一种面向订单装配的制造企业生产管理系统的实现方案,通过运用计算机网络、数据库处理和现代企业管理技术开发出集企业订单处理、生产计划控制、采购处理和财务处理等功能于一体的面向订单装配的生产管理系统。充分利用B/S、C/S混合模式的优点,克服了目前单一模式下的缺陷,能及时有效地实现企业内部各部门之间以及企业和客户之间的信息互动,对制造企业动态应对市场,及时准确地掌握信息资源并做出正确的生产计划决策,具有重要意义。本文具体介绍了Web混合模式下面向订单装配的生产管理系统构建方案和系统基本功能。     

学生成绩管理系统的开发

随着信息技术的不断发展,计算机技术已经应用到各个领域,通过使用计算机可以减少大量的人工操做,提高工作效率.很多大、中、小学校都在开发自己的学生成绩管理系统,以便对本学校学生的基本信息和学习情况进行管理.而利用计算机来管理信息和处理信息,就需要利用数据库管理系统软件来开发各个行业的实用软件,因此开发了学生成绩管理系统这个软件.     

浅论网络数据库技术的发展

随着现代社会信息需求量的不断增长,如何在网络环境下实现信息的互操作和数据共享,己经发展成为计算机应用领域的热点问题。开发一个满足实际工作需要的应用系统,需要我们从业者综合运用多媒体、网页制作、数据库访问等技术,进行一系列的需求分析、设计开发、系统调试直至最后投入使用的工作。     

城乡建设用地增减挂钩项目管理信息化建设思考——以四川省城乡建设用地增减挂钩项目管理系统为例

为加强城乡建设用地增减挂钩信息化建设,基于计算机技术、网络技术和数据库技术,研发城乡建设用地增减挂钩项目管理系统。采用校验录入和复核录入的信息录入方式,准确提取项目信息并存储到数据库;设计具有高级逻辑性的信息查询、统计系统;运用OLE技术无缝链接多种格式的文件;开发基于网络的web应用程序及数据自动化采集功能扩展模块。实现城乡建设用地增减挂钩项目信息电子化存储、查询统计分析、网络信息共享等规范、科学、便捷的项目管理系统功能。     

企业计算机计划管理系统

我国企业中应用计算机首先是从事务管理和财务管理领域开始的,大多数企业首先研制、开发和投入使用的是计算机工资系统、人事系统和统计报表系统。随着经验的积累和工作的深入,近年来,已开始向经营管理、生产管理领域发展。在一些企业中,库存管理系统、生产计划系统、合同管理系统、成本核算系统以及经营预测系统已不同程度地研制开发出来,有些已成功地投入运行,取得了经济效益。但是,在已开发的各类     

开放式3G网络远程监控系统在数字城市中的应用

针对目前3G远程监控技术存在开放性弱、可靠性差、应用开发难的不足,本文拟从系统的整体架构及开放性、兼容性、数据传输的可靠性等方面构建开放式3G网络远程监控系统,并将其应用到数字城市基础设施与公共事业的动态监控中去,推进3G技术在无线网络监控中的应用和发展,促进数市化城市建设,实现信息资源共享。     

计算机会计的安全防范

随着计算机技术在会计信息系统中广泛使用,越来越多的单位建立了计算机会计信息系统,使会计数据处理技术发生了质的飞跃.当前计算机会计信息系统在企业网络、商际网络及国际互联网范围内整合使用,不仅具备以往会计信息系统的基本功能,而且能支持远程处理,如远程报表、远程报账、远程查询和远程审计等.计算机信息技术在给企业带来效益的同时,也增加了企业会计信息的安全风险.     

学生成绩管理系统的开发

随着信息技术的不断发展,计算机技术已经应用到各个领域,通过使用计算机可以减少大量的人工操做,提高工作效率。很多大、中、小学校都在开发自己的学生成绩管理系统,以便对本学校学生的基本信息和学习情况进行管理。而利用计算机来管理信息和处理信息,就需要利用数据库管理系统软件来开发各个行业的实用软件,因此开发了学生成绩管理系统这个软件。     

基于Zigbee的远程温度计设计

随着计算机通信技术的迅猛发展,无线健康监护网络在增强医患交流的同时推动了精确医疗信息的及时传递,减少了信息采集过程中错误的发生,极大地改善了获取重要生理信息的途径。目前,在数字医疗研究领域,多数研究者关注的重点是如何最有效地将医护和患者与存储于各种数据库中的信息联系起来,而往往忽略了信息采集与传输的实现方式,目前这个过程极大程度上仍然依赖于人工操作。鉴于此,本文在对无线传感器及其网络协议技术分析的基础上,设计出一种基于Zigbee技术的远程温度采集系统。用基于Zigbee网络的无线方式通过温度测量节点收集温度数据,通过串口通信线路连接主要节点和前端电脑,存储温度数据至数据库,以便实现数据的统一管理。     

A data mining algorithm for fuzzy transaction data

The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers’ fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery.     

Forecasting data vintages

This article provides a discussion of Clements and Galvão’s paper “Forecasting with vector autoregressive models of data vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions. They draw a “distinction between forecasting future observations and revisions to past data,” which focuses forecasters’ attention on yet another real time data issue. This comment discusses the importance of taking data revisions into consideration, and compares the multiple-vintage VAR approach of Clements and Galvão to a state space approach.     

Contradictions of interactive data analysis

Up to now, interactive statistics seems to have bloomed on a creative mess of contradictions and conflicts. But I believe that if we want to advance beyond the present stage, we must try to overcome those. I shall attempt to identify a few of the more crucial ones, ranging from the basics of interactivity and data graphics to issues of large data and data base management, and to distill specific challenges from them.     

Statistical properties of data stretching

This research examines the properties of an estimation procedure frequently used because observations on some variables are available only at higher levels of aggregation than others. When this occurs, data are often stretched by repeating observations on variables at higher levels of aggregation. We show that this procedure results in biased estimators of coefficients and error variances. Under some circumstances the estimation based on stretched data has a smaller covariance matrix than that based on aggregated data. Comparisons of mean squared errors depend on unknown coefficients.     

Some problems of data standardization

Procedure of aggregation of component indicators into one composite index of a multi-dimensional phenomenon under study requires previous standardization of the original raw data in terms of which the component indicators are expressed. The standardization is inevitably connected with an arbitrary choice of a mathematical formula by which the same measurement units, for all component indicators, are established. It is argued that by choosing one of the several possible standardization formulae, a researcher, most often unkowingly, ascribes differential weights to particular component indicators. Two approaches, in solving the problem of arbitrariness of the choice of the standardization formula, are possible: (1) assessment of the margin of “error of arbitrariness,” or “standardization effect,” and the interpretation of substantive results obtained after the aggregation, within that margin; (2) evaluation of equivalencies of things, established by the alternative standardization formulae, in order to choose the formulae ensuring the most acceptable equivalence as judged by certain objective norms or criteria. The two approaches are discussed, and numerical examples illustrating each of them are presented using two kinds of standardization: the one based on percentages, and the other on the deviation from the mean divided by standard deviation.     

The social dilemma of big data: Donating personal data to promote social welfare

When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Regulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the perceived risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are processed by a human-supervised or a self-learning smart assistant.     

Subsampling high frequency data

The main contribution of this paper is to propose a novel way of conducting inference for an important general class of estimators that includes many estimators of integrated volatility. A subsampling scheme is introduced that consistently estimates the asymptotic variance for an estimator, thereby facilitating inference and the construction of valid confidence intervals. The new method does not rely on the exact form of the asymptotic variance, which is useful when the latter is of complicated form. The method is applied to the volatility estimator of Aït-Sahalia et al. (2011) in the presence of autocorrelated and heteroscedastic market microstructure noise.