浅谈关系数据模型的矢量数据空间叠加

本文对已有空间数据模型的特点进行了评述.指出数据存储方式决定了空间叠加方式的选取,然后基于当前流行的关系数据模型,对矢量数据的空间叠加进行了详细的探讨,提出了将拓扑复合数据结构引入关系数据模型的空间叠加设想.     

Oracle空间数据库的设计及应用

随着科学技术的不断发展，建设数字化城市是我国各省市政府部门的最新目标。因此，伴随着数字化城市的建设，将会产生大量的空间数据和属性数据。但是这两种数据独立的存储方式给数据间的共享和互相操作带来了很多的问题，所以，对于 Oracle 空间数据库的存储方式以及相应系统的开发，已经成为了数字化城市建设的重点和难点。据此，笔者就Oracle空间数据库的设计及应用做了详细的介绍。     

基于空间自相关模型的浙江省区域经济统计分析

本文主要研究的是基于空间自相关模型的浙江省区域经济,具体包括两个部分:浙江省区域经济地区差异评价的数据来源及处理和基于空间自相关模型的浙江省区域经济实证分析。并且,通过全局空间自相关的方式,进行自相关模型的回归分析,最终对浙江省区域经济做出全面的分析。     

基于空间计量经济学的若干思考

空间计量经济学在研究方面得到了迅速的发展,主要是通过建立数学模型研究横截面数据和面板数据在回归模型中的空间相互作用的空间自相关以及空间结构的不均匀性。因此,我们在研究空间计量经济学的时候,首先要有大量的数据,通过这些数据的空间分布,建立数学模型,研究它们之间的关系和规律,进而把研究的结果应用到经济学领域当中。本文主要介绍了空间回归分析的相关问题,研究了横截面数据空间线性模型的通用模式。     

基于分组空间自回归模型的房价预测

近年来,随着经济的飞速发展,一线城市房屋价格也在快速上涨,房价成了人们关心的热点问题。本文基于样本点间的类别特征对空间自回归模型进行改进,提出了分组空间自回归模型,并基于安居客平台收集的2013年北京二手房成交价格数据进行验证。从结果来看,本文所提出的分组空间自回归模型对北京房价数据有较好的预测效果,并能够反映不同类别空间点之间的相互影响关系。该方法有效地提高了空间自回归效应的自由度从而提高预测精度,可为政府决策或居民投资提供一定的参考依据。     

基于GIS的空间数据库系统设计与实现

本文介绍了GIS(地理信息系统)和空间数据库的概念,以及分析了基于GIS的空间数据库在煤矿监测系统上应用的意义,完成了对空间数据库的建立,并对系统结构及其功能进行了研究与实现。     

企业空间信息化战略探讨

空间信息是企业商业决策的基础。市场竞争的需要迫切要求企业实现空间信息化，空间信息处理技术的成熟为企业空间信息化提供了技术的可行。目前，大多数企业信息系统中的空间信息还以属性数据的形式存在，未能充分利用。本文提出企业空间信息化的数据基础框架，探讨了企业空间信息化战略的动因以及空间信息化战略实施时应该注意的问题，并对前景进行了展望。     

铁路空间信息管理系统建设及应用探讨

铁路空间信息管理系统是信息化社会发展和铁路大发展的产物，通过它可以将铁路黝测设计中需要使用的空间信息数据进行有序地组织和科学地管理，并能够提供初步的空间分析和处理功能。本文就铁路空间信息管理系统的主要内容、特点、体系结构、作用和要求进行了详细论述。     

市场营销空间的三层面研究

<正>一、市场营销空间的三个层面市场营销空间是管理领域里面一个重要的概念,但是目前尚没有一个科学完整的定义,很多文献只粗略的讲到市场营销空间,而没有对此进行详细的阐述,包括市场营销空间的内涵和外延,因而在应用之中,存在很大的模糊性。对中国期刊全文数据     

财政分权、空间溢出与经济增长

相关研究发现,邻省间存在明显的政策模仿和基础设施的空间溢出作用。而财政分权作为影响地方政府政策和基础设施建设的重要因素,也必然对经济增长具有较强的空间溢出作用。在经典财政分权模型的基础上,建立考虑空间相关的拓展模型,以此模型的结论为基础,利用中国大陆31个省份1998—2005年的面板数据进行空间计量分析,结果发现,我国的财政分权对经济增长有着显著的促进作用和空间溢出效应。     

Big data

Big data is defined and distinguished from a mere moment in the “ancient quest to measure.” Specific discontinuities in the practice of information science are identified which, the paper argues, have large consequences for the social order. The infrastructure that runs on big data is described as diffusing with unprecedented speed but as being difficult to analyze and critique, and therefore the designers of society’s big data infrastructure, whether human or machines, play an unacknowledged legislative function of great consequence.     

Multi level categorical data fusion using partially fused data

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.     

Addressing barriers to big data

Increasingly, big data is viewed as the most strategic resource of the 21st century, similar in importance to gold and oil. While sitting on these vast pools of data, many organizations are simply not ready to take advantage of this new strategic resource. Embracing big data requires addressing a number of barriers that fall into the domains of technology, people, and organization. A holistic, socio-technical approach is required to overcome these barriers. This article introduces the specific tactics we recommend for addressing big data barriers, which involve changes to technology infrastructure, a focus on privacy, promotion of big data and analytic skills development, and the creation of a clear organizational vision related to big data.     

Factors influencing big data decision-making quality

Organizations are looking for ways to harness the power of big data (BD) to improve their decision making. Despite its significance the effects of BD on decision-making quality has been given scant attention in the literature. In this paper factors influencing decision-making based on BD are identified using a case study. BD is collected from different sources that have various data qualities and are processed by various organizational entities resulting in the creation of a big data chain. The veracity (manipulation, noise), variety (heterogeneity of data) and velocity (constantly changing data sources) amplified by the size of big data calls for relational and contractual governance mechanisms to ensure BD quality and being able to contextualize data. The case study reveals that taking advantage of big data is an evolutionary process in which the gradually understanding of the potential of big data and the routinization of processes plays a crucial role.     

Big data? Big deal: Searching for big data’s performance effects in HR

Big data continues to gather increasing interest in the business press as well as within the management literature. While this interest has spilled over into the realm of human resources (HR) management, solid evidence of its positive performance impacts is lacking. I explore three possibilities for this lack of evidence: (1) HR possesses big data but largely lacks the ability to use it; (2) HR does not actually possess big data; and (3) big data is generating value for HR and positively affects organizational performance, but the winners in the race to utilize big data in HR are not publicizing their successes. Following this, I discuss current forms of big data implementation, highlighting an evolutionary progression of implementations in various settings and emphasizing the importance of balancing deductive with inductive analytical approaches. Finally, I discuss conditions under which big data may hold greater value for the HR function, and I suggest ways managers and organizations can make the most of big data.     

多信道数据传输技术分析

文章分析了应用LVDS技术解决雷达系统中多信道、高速数据的传输问题.     

谈对会计数据电算化管理问题分析

文章分析了会计电算化存在的问题及处理对策.     

Big web data: Challenges related to data,technology, legality,and ethics

The digital data available online is currently measured in zettabytes. These vast repositories of big web data are increasingly viewed as a strategic resource comparable in value to land, gold, and oil. This big web data can be extracted and analyzed by organizations to gain a better understanding of their internal and external environment and improve organizational performance. Because of these opportunities, automated retrieval and organization of web data (i.e., web scraping) for research projects is becoming a common practice. This article outlines the data-related, technical, legal, and ethical issues related to web scraping. Awareness of these issues can help researchers save time and resources and, most importantly, mitigate the potential risk of ethical controversies or lawsuits related to the retrieval and use of big web data.     

Bringing dark data into the light: Illuminating existing IoT data lost within your organization

Companies are making substantial investments in placing sensors out in the field and within products and equipment. These Internet-of-Things (IoT) technologies provide an unprecedented opportunity to harness data-driven insights to improve business performance. While the IoT is often framed as a next-generation technology, many businesses already are awash in these data because their existing operational technologies and other equipment generate substantial amounts of data. These data, along with the insights they generate, remain dark—out of sight and unused. Many managers are unaware of the existence and uses of such data. To shed light on the business opportunities afforded by exploiting dark data, this article provides managerial advice and demonstrative case studies in four strategic categories: improving returns on assets, reengaging customers, creating new products and business lines, and transforming business models.