Design and Implementation of Novel Precision Internet Marketing Patterns under the Big Data and Cloud Environment

Nowadays most of the cloud applications process large amount of data to provide the desired results. The Internet environment, the enterprise network advertising, network marketing plan, need partner sites selected as carrier and publishers. Website through static pages, dynamic pages, floating window, AD links, take the initiative to push a variety of ways to show the user enterprise marketing solutions, when the user access to web pages, use eye effect and concentration effect, attract users through reading web pages or click the page again, let the user detailed comprehensive understanding of the marketing plan, which affects the user＇ s real purchase decisions. Therefore, we combine the cloud environment with search engine optimization technique, the result shows that our method outperforms compared with other approaches.

Study on the influence of network marketing to the development of SME under the context of big data

With the rapid development of information networks and the overall development of e-commerce, online marketing has continued to assault the traditional business marketing model and methods of operation, and it makes great effects on existing business concepts and ideas from different angles and levels. For these weaker small and medium-sized enterprises, network marketing with its low cost, wide range of applications, the effect of strong natural advantages becomes marketing approach of small and medium-sized enterprises which they can hold up and afford, which also provides unprecedented opportunity for small and medium-sized enterprises to have the opportunity to compete with large-scale enterprises on the same stage.

Literature Review of Marketing theory based on Big Data

Since the concept of big data was proposed, the theory on big data is concerned by public, academics, market watchers, researcher and so on, people explore all aspects of the Big Data Time, more than in academic, it has an impact on all areas in marketing,we collect some papers and extract its viewpoints that involve the theory, methods in this article, we hope that it helps to do research on the theory of big data in the field of marketing.

Research on the Innovative Strategy of Blended Teaching in Network Marketing in the Era of Big Data

Network Marketing is a practical professional compulsory course.With the advent of big data technology,a hybrid teaching model combining online and offline has emerged.Based on the analysis of the advantages of using a hybrid teaching model in Internet Marketing,this article comprehensively considers a variety of factors and put forward the innovative strategy of blended teaching in Network Marketing in the era of big data.This may provide new paths and methods for enhancing teaching effects,cultivating students’independent learning,and improving core literacy.

Big Data Usage in the Marketing Information System

Data generation, storage capacity, processing power and analytical capacity increase had created a technological phenomenon named big data that could create big impact in research and development. In the marketing field, the use of big data in research can represent a deep dive in consumer understanding. This essay discusses the big data uses in the marketing information system and its contribution for decision-making. It presents a revision of main concepts, the new possibilities of use and a reflection about its limitations.

Research on the Application of Energy Internet Big Data in Integrated Energy Market

Energy Internet is deeply integrated by Internet concept, information technology and energy industry, and Energy Internet Big Data are one of core technologies that achieve energy-information-economic interconnection and improve the development and evolution of Energy Internet. This paper describes the concept and characteristics of Energy Internet Big Data, and feasibility of applying Energy Internet Big Data to integrated energy market. On this basis, as for integrated energy market and multi-subjects of Energy Internet, typical application and technical system based on Energy Internet Big Data in integrated energy market is put forward, which provides a reference for the analysis and decision of integrated energy market in Energy Internet.

Research on the Modern Precision E-commerce Marketing Model under the Big Data and Pattern Recognition Background

In this paper, we conduct research on the modern precision e-commerce marketing model under the big data and pattern recognition background. Large amount of consumption data provides the electricity enterprises grasp the user consumption pattern and the basis of the electric business enterprise through the use of big data can be personalized, accurate and intelligent advertising push service, service mode for the creation of more interesting and effective. Under this basis, electricity companies can also pass the assurance of pair of big data, looking for better increase user stickiness, development of new products and services, the ways and methods to reduce operational costs and accordingly, we propose the novel perspectives on the corresponding issues for the systematic level enhancement that provides the novel methodology of precision e-commerce marketing.

The Development of Market Method of Asset Appraisal Based on the Application of Big Data

Big data could be utilized in work and life. Asset appraisal could also make full use of big data to improve the efficiency and effectiveness of appraisal. The paper is going to study the application of big data in different fields to learn how big data works in practices and what the effect is after utilizing the new tool. Then, the paper is going to apply big data in appraisal in specific work environment. By collecting information, researching literature and practicing with appraisers, this paper f＇mds some means to improve market method by making full use of big data. The article researched further by applying the method in different projects of asset appraisal. Real estate, intangible asset, corporate valuation and machines could be valued by the market method improved by big data. There are different details for appraisers to be careful in practical work. Some companies have already put the technology into practice and achieved great benefit, which makes the application of big data meaningful.

Tobacco control challenges in China: Big data analysis of online tobacco marketing information

Objective:To develop tobacco control strategies by analyzing online tobacco marketing information in China.Methods:Using web-crawler software,this study acquired 106,485 pieces of online tobacco marketing information published on 11 different Internet platforms including Weibo,WeChat,Baidu,etc.,from January-June 2018.The data were used to investigate the characteristics and social networks of online tobacco marketing via content and social network analysis.Results:The total volume of online tobacco marketing during the study period was high,showing a positive trend.Of all the marketing subjects,those involving"flavor capsule","Marlboro",and"Esse"were the most popular.The Weibo platform had the highest volume of online tobacco marketing information as well as the largest proportion of explicit marketing information.This was followed by other social media platforms such as Baidu Search,Baidu Tieba,and Xiaohongshu,where implicit marketing information predominated.The overall network structure of tobacco websites exhibited a significant centralization feature,where traditional and novel tobacco websites formed two clusters with almost no intersections.The China Tobacco Science and Education Website(http://www.tobaccoinfo.com.cn/)and E-Cigarette Home(http://ecigm.com/)were the two nodes of the highest degree centrality within the respective"circle",while the China Tobacco Monopoly Bureau Website(http://www.tobacco.gov.cn/)was the node with the highest closeness centrality.By contrast,Baidu Tieba's overall network structure was more decentralized,and the degree of correlation between different nodes was relatively low.Conclusion:Online tobacco marketing demonstrated high volumes and wide coverage,and an intertwined network,thereby creating major obstacles for tobacco control.To address this issue,the government should strengthen network supervision of tobacco marketing and revise its current regulations.Meanwhile,Internet platforms should improve self-regulation by comprehensively removing and blocking tobacco-related information.Lastly,the media and public should advocate associated policies and support Internet platform supervision.

Brand Marketing in an era of big data

In the digital age, people＇ s lifestyles and ways of thinking are in a series of changes, this change also makes it a big shift in consumer attitudes. It gives consumers a broader perspective, while also improving the self-consciousness of consumers. These effects will no longer fully believe in traditional consumer marketing ＂bombing＂ the dissemination and indoctrination, they are more inclined to be questioned brands and products, and they can express their views on the basis that affects other people. In this era of environment, if businesses and vendors for their view is indifferent attitude, they will lose a lot of attention to the crowd, and also it makes the influence of the traditional marketing model spread greatly reduced.

An Overview of Big Data Industry in China

The year of 2011 is considered the first year of big data market in China.Compared with the global scale,China's big data growth will be faster than the global average growth rate,and China will usher in the rapid expansion of big data market in the next few years.This paper presents the overall big data development in China in terms of market scale and development stages,enterprise development in the industry chain,the technology standards,and industrial applications.The paper points out the issues and challenges facing big data development in China and proposes to make polices and create support approaches for big data transactions and personal privacy protection.

Agricultural remote sensing big data:Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

Internet big data and capital markets:a literature review

Background:Research in various academic disciplines has undergone tremendous changes in the era of big data.Everyone is talking about big data nowadays,but how exactly is it being applied in research on financial studies?Results:This study summarizes the sources of Internet big data for research related to capital markets and the analytical methods that have been used in the literature.In addition,it presents a review of the research findings based on Internet big data in the field of capital markets and proposes suggestions for future studies in which big data can be applied to examine issues related to capital markets.Conclusion:(1)Internet big data sources related to present capital market research can be categorized into forum-type data,microblog-type data and search class data.(2)As for research about investors’sentiments on the basis of Internet big data,the main methods of sentiment analysis include building an inventory of lexical categories,using dictionaries for analysis of lexical categories,and machine learning.(3)Many studies address whether Internet big data can predict capital markets.However,they reach no consistent conclusions,which could be due to limitations of sample and analysis method used.(4)Data collection technique and analysis methods require further improvements.

Social Media and Stock Market Prediction: A Big Data Approach

Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retailers are building vast databases of customer sales activity.Organizations are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and products.Challenges of big data include volume and variety in both structured and unstructured data.In this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional models.We studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision tree.We implemented naive Bayes and logistic regression classification models.Experimental results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

Big Data Application in Asset Appraisal Using Market Method

The big data era is coming, which influences the life of human beings in every aspect, such as working, studying, shopping and so on. The data could be uploaded and recorded by the digital devices like smart-phone and pad. The volume of data could provide useful information to hdp learn the habit of human beings and improve the efficiency of work. The domain of asset appraisal could make full use of big data to collect and sort information involving the appraised asset and market. On the one hand, market method of asset appraisal needs a plenty of information of reference substance and industry development. On the other hand, big data with the trait of volume and velocity could be utilized to collect information. The paper reveals that taking advantage of big data application in asset appraisal using market method is an evolutionary process in which the gradual understanding of the potential of big data plays a crucial role.

Business challenges and research directions of management analytics in the big data era

Big data analytics have been embraced as a disruptive technology that will reshape business intelligence,particularly marketing intelligence,which has have traditionally relied on market surveys to understand consumer behavior and product design.In this paper,we investigate how big data analytics will affect the landscape of business intelligence,leading to big data intelligence.Rooted in the recent literature,we delineate business opportunities and managerial challenges brought forward by the emergence of big data analytics and outline a number of research directions in big data intelligence for business.

Exploring the Linear and Nonlinear Causality Between Internet Big Data and Stock Markets

In the era of big data,stock markets are closely connected with Internet big data from diverse sources.This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines,public media and social media.To achieve this purpose,a big data-based causality testing framework is proposed with three steps,i.e.,data crawling,data mining and causality testing.Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets,web search data,news,and microblogs as samples of Internet big data,some interesting findings can be obtained.1)There is a strong bi-directional,linear and nonlinear Granger causality between stock markets and investors'web search behaviors due to some similar trends and uncertain factors.2)News sentiments from public media have Granger causality with stock markets in a bi-directional linear way,while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way,running from stock markets to microblog sentiments.3)News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority.The results of this paper might provide some valuable information for both stock market investors and modelers.

Baidu index and predictability of Chinese stock returns

A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

大数据背景下的电力营销信息管理平台设计

为了提升电力营销大数据处理和调度效率,设计大数据背景下的电力营销信息管理平台。基于Hadoop和MapReduce设计平台存储和技术框架,利用大数据技术划分电力营销大数据,实现电力营销信息状态安全分析和监控;采用云计算技术分析和处理电力营销数据。测试结果显示,所设计平台的大数据处理吞吐量较高、大数据处理加速比稳定;数据调度计算效率较高、性能稳定;将电力营销数据以对象形式缓存到内存中时,执行时间较短、耗费内存较小。该平台具备较好的数据处理及调度能力。