基于计算机和通信技术的营销情报系统

营销情报系统模型分析在市场营销教学中,如何将计算机网络知识和营销知识有机融合,让学生能够更好地理解和掌握课程,是教师需要重点考虑的内容。笔者通过对营销情报系统的介绍,帮助学生学习这门课程。教学中,营销情报系统的构建需要从以下几个方面做起。基本设施:计算机及办公自动化设备和信息通信技术;数据库:顾客情报数据库、竞争对手情报数据库、价值链成员情报数据库和宏观环境情报数据库;组织保障:组织结构网络、信息传递网络和人际关系网络;方法体系:统计分析工具库、决策模型库等。营销情报系统模型是对营销情报系统的概括和抽象。教…     

浅议数据库营销的竞争优势

随着网络技术的发展,计算机网络为企业提供了更安全地运用和处理数据资源的能力,网络数据库营销应运而生,作为一种新的营销模式,体现出极大的优势.     

基于网络环境中数据库营销的应用研究

数据库营销是现代电子商务发展中一个越来越重要的内容,在数据库营销中,其可测度强、低成本,可直观对客户进行偏好、习惯、消费能力等方面进行分析的优点受到很多中小企业的重视,本文通过对数据库营销的运作过程及操作步骤进行了详细介绍,并结合网店管理实例进行数据指标分析,希求通过此种方式,对数据库营销在网络环境中的应用进行深入研究,为网上中小企业的发展提供有效的应用方法。     

基于不同多层技术的软件设计--揭阳供电局电力营销管理信息系统设计与实现

本文结合广东电网公司揭阳供电公司电力营销管理系统的实施情况,简要介绍了电力营销管理系统的设计与实现,对网络组成、软件结构和数据库的设计作了介绍,并重点介绍了不同多层技术和J2EE在本系统中的实现.     

基于Web的网络数据库安全技术研究

网络和数据的时代中,WEB数据库信息的存储量将越来越大,但是网络数据库信息安全也越来越重要。本文主要介绍了网络数据库安全的基本技术、预防措施和安全实现。     

基于网络的数据库开发技术

文章根据数据库在网络环境下的特点,讨论了当前开发网络数据库的几种方法,重点讨论了通过代理服务访问数据库的技术,在此基础上具体实现一个网络环境下多用户数据库系统的基本框架.     

统一用户数据库设计及其应用

随着电信进入全业务时代，以往分散式建网的模式已不符合网络发展趋势。用户对业务体验要求越来越高，希望业务能够实现一站式受理和开户，并能够通过统一的账号访问到尽可能多的业务，因此，运营商迫切需要实现统一的用户数据库。文章在分析宽带网络用户数据库问题的基础上，结合业务发展需求，提出了统一用户数据库的设计方案，并详细介绍了如何利用它来支持账号经营和差异化营销。     

企业营销复杂网络演化模型及其仿真

在对企业营销网络理解的基础上,将其抽象为复杂网络。分析了企业营销复杂网络的演化机制,建立了企业营销复杂网络演化模型,并对模型进行了仿真分析。结果表明企业营销复杂网络模型节点度分布、节点强度分布和边权分布均符合幂律分布,表明其能较好地拟合实际企业营销网络的拓扑结构特征。研究对于探讨企业营销网络的演化规律,对企业营销网络的建设和管理具有理论意义和实践价值。     

关于无线传感器网络数据库的研究与实现

近些年来,无线通信技术和传感器技术等得到前所未有的发展。无线传感器网络可以将各种通讯手段通过相关设备实现实物的处理和数据交互,已经被应用于军事、自然环境监测等领域。本文通过对无线传感器网络数据库的基本内容的概述,论述了无线传感器网络数据库实现的关键技术。     

网上营销诚信等级评审恰当时

日前,从中国电子商务协会传来消息,如今在网上开展营销活动的企业可以申请诚信等级了.据悉,在网上开展营销活动的企业,只要登录相关网站,下载全国企业网络营销基本诚信数据库申报表,即可申请从低到高不同诚信等级.     

Network Topology and Locational Market Power

This paper studies the effect of network topology and production constraints on the locational market power of generators. A market power spectrum is considered where one end has infinitely large production capacity but the usual network constraints while the other end has infinitely large network capacity but the usual production constraints. First, we analyze the locational market power function mathematically. Then, we use a real world example of the Portland, Oregon electrical market and determine its position on the market power spectrum. We find the Portland market to be primarily production constrained rather than network constrained. We also identify the local and global threshold generation capacities for each generator beyond which it cannot influence the individual and total locational market power, respectively. This study facilitates the understanding of the economic and physical determinants of locational market power. It can help regulators make informed decisions when it comes to the choice of enhancing the physical infrastructure, or adding more generation capacity to the market.     

网络交易监管存在的问题及监管对策研究

近年来,随着以互联网为载体的商品交易活动呈指数性地增加,因其巨大的成长空间和市场潜力,各种网络交易违法行为相伴而生,日渐成为严重的社会问题。在对当前我国网络交易监管中存在的问题进行分析和研究的基础上,结合我国国情,构建网络交易监管模式,并提出进一步提高我国网络交易监管效率的对策,为未来网络交易监管的研究提供了理论支持。     

Network Expansion to Mitigate Market Power

Constrained transmission capacity in electricity networks may give generators the possibility to game the market by specifically causing congestion and thereby appropriating excessive rents. Investment in network capacity can ameliorate such behavior by reducing the potential for strategic behavior. However, modeling Nash equilibria between generators, which explicitly account for their impact on the network, is mathematically and computationally challenging. We propose a three-stage model to describe how network investment can reduce market power exertion: a benevolent planner decides on network upgrades for existing lines anticipating the gaming opportunities by strategic generators. These firms, in turn, anticipate their impact on market-clearing prices and grid congestion. In this respect, we provide the first model endogenizing the trade-off between the costs of grid investment and benefits from reduced market power potential in short-run market clearing. In a numerical example using a three-node network, we illustrate three distinct effects: firstly, by reducing market power exertion, network expansion can yield welfare gains beyond pure efficiency increases. Anticipating gaming possibilities when planning network expansion can push welfare close to a first-best competitive benchmark. Secondly, network upgrades entail a relative shift of rents from producers to consumers when congestion rents were excessive. Thirdly, investment may yield suboptimal or even disequilibrium outcomes when strategic behavior of certain market participants is neglected in network planning.     

基于分布式神经网络的苹果价格预测方法

针对传统农产品价格预测模型在大数据场景下无法快速准确对苹果市场价格进行预测的问题,提出一种基于分布式神经网络的苹果价格预测方法。首先,研究影响苹果市场价格的相关因素,选取苹果历史价格、替代品历史价格、居民消费水平和原油价格四个特征作为神经网络模型的输入;然后,构建蕴含价格波动规律的分布式神经网络模型,实现对苹果市场价格的短期预测。实验结果显示,基于分布式神经网络的苹果市场价格短期预测模型具有较高的预测精度,平均相对误差仅为0.50%,满足苹果市场价格预测的要求。实验结果表明,分布式神经网络模型能够通过自学习特性揭示出苹果市场价格的波动规律和发展趋势,所提方法能为稳定苹果市场秩序和市场价格宏观调控提供科学依据,有助于降低价格波动带来的危害,帮助果农规避市场风险。     

Reconfiguration framework of a supply network based on flexibility strategies

Because the supply network of an enterprise should be flexible enough to capture and overcome market dynamics, one of the major concerns of global enterprises is to make their supply network reconfigurable. Although many strategies for flexible management of a supply network have been proposed, especially for mitigating supply network risks, it still remains unclear how to apply the strategies to a supply network and how to reconfigure the supply network. This paper examines the influence of flexibility strategies in a dynamic global market environment on the structure of supply network, and proposes a method of reconfiguring the supply network of an enterprise to cope with its flexibility strategies. A reconfigurable supply network model is proposed, and flexibility strategies are classified, and critical indices of strategies are defined. In the proposed model, each business actor is defined as a network node and each node has its own goal. A node optimizes its goal to reduce and overcome the risk of market environments. The result of optimization indicates that the supply network structure is reconfigured dynamically.     

传统市场与网络市场跨界融合研究

随着网民规模的不断扩大,跨界融合的特征已经在多个方面有所显现。从媒介共存、用户观看习惯、电子商务和整合营销等方面分析了传统市场和网络市场融合的必要性,对传统电视台、传统实体销售渠道和广告主布局在线业务提出了建议,论述了传统市场与互联网市场应跨界融合、共同发展。     

A new evolution model for B2C e-commerce market

In order to explain the formation of the business to customer e-commerce market structure, we introduce two concepts—market trading volume and user penetration into the analytical framework for e-commerce market. Based on the modification of Barabasi–Albert (BA) model, a new model which is added with fitness parameters and more reasonable growth mechanism is proposed. The model reveals a “bubble-stable” evolutionary process which is correspondent with real e-commerce market from an initial network to a scale-free one. The simulation results show that the number of websites a buyer chooses could affect the evolutionary process of user penetration distribution, but almost not affect the stable trend of the market. In addition, the initial network scale almost does not affect the nature of network, but causes market fluctuation. The model also reveals that unfair competition among websites is the reason for the formation of structure. Hence, a new method which is calculating numbers of overlap users between each pair of websites is developed to measure the competitive strength. Then, three distinct components are found in the competitive network: a small nucleus, a secondary core and a huge bulk body.     

基于初级专业认证的计算机网络技术专业人才培养模式

针对人才培养与市场要求脱节的情况。从计算机网络技术专业人员的角度,分析国内各种计算机网络技术初级专业认证的目标、要求、内容和特点,从教学内容、教学方法和手段、课程体系和评估方式四个方面阐述基于初级专业认证的计算机网络技术专业人才培养模式．旨在培养出符合市场要求的初级专业人才。     

Deep learning networks for stock market analysis and prediction: Methodology,data representations,and case studies

We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine—on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.     

网络外部性下基于不确定环境的竞争供应链纵向结构选择

在网络外部性情境下, 考察网络外部性强度、竞争强度和市场风险对竞争供应链结构选择的影响, 分别从制造商与供应链的角度出发, 识别竞争供应链的纵向结构选择策略和动态演化均衡. 基于制造商的结构选择受市场风险波动范围和竞争强度的影响, 竞争强度边界和风险的范围依赖于网络外部性强度系数, 在不同的网络外部性环境下, 竞争强度和风险对制造商的结构选择的影响作用不同; 基于供应链系统的控制结构选择与市场风险无关, 仅与依赖于网络外部性强度的竞争强度有关.