Aviation Safety and Data Mining in Marketing Dimension

The aviation industry is a sector that is developing, changing and growing every day in terms of technological and legal framework. There are generally three factors that enable airlines to hold on to the market. These factors are safety, service quality and price. Airline companies can analyze the customers in the market with a focus on price and quality and develop a business model according to their expectations. For example, business class and economy class passenger expectations are different from each other, so the service and price to be offered to them will be different. However, all customers have one common expectation and that is safety. No matter how high quality the service is or how cheap the price is, no one wants to fly with an airline or plane that is not safe. From an airline company’s point of view, an accident or breakdown of one of the company’s aircraft can cause irreparable image loss and financial damage. If we look at past examples, we see that there are many airline companies or maintenance organizations that could not recover after an accident and went bankrupt. Safety is an indispensable factor. Therefore, there is a unit in the sector called the safety management system (SMS), which collects data by taking a proactive and reactive approach. The way and purpose of the safety management system is to take a proactive approach to recognize and prevent unsafe situations before they cause accidents or breakdowns, or to take a reactive approach to find the causes of accidents and breakdowns that have occurred as a result of certain factors and to take the necessary measures to prevent the same situations from happening again in the sector. The field of data mining, which is necessary to predict the future behavior of customers in the field of marketing, is an area that marketing also values. In this study, data mining studies to ensure safety in the aviation industry and the security of customer information in marketing will be emphasized, firstly, the concept and importance of data mining will be mentioned.

Exploring the Pathways of Power Grid Marketing Management Based on Big Data

The electric power industry is undergoing profound transformations driven by big data,posing challenges to the traditional power grid marketing management model.These challenges include neglecting market demands,insufficient data support,and inadequate customer service.The application of big data technology offers innovative solutions for power grid marketing management,encompassing critical aspects such as data collection and integration,storage management,analysis,and mining.By leveraging these technologies,power grid enterprises can precisely understand customer needs,optimize marketing strategies,and enhance operational efficiency.This paper explores strategies for power grid marketing management based on big data,addressing areas such as customer segmentation and personalized services,as well as market demand forecasting and response.Furthermore,it proposes implementation pathways,including essential elements such as organizational structure and team building,data quality and governance systems,training,and cultural development.These efforts aim to ensure the effective application of big data technology and maximize its value.

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.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

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.

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.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Modeling and application of marketing and distribution data based on graph computing

Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems.New energy sources are continuously being connected to distribution grids;this,however,increases the complexity of the information structure of marketing and distribution businesses.The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks.As a solution,this paper presents a data model of"one graph of marketing and distribution"and a framework for graph computing,by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory.Specifically,this work aims to determine the correlation between distribution transformers and marketing users,which is crucial for elucidating the connection between marketing and distribution.In this manner,a novel identification algorithm is proposed based on the collected data for marketing and distribution.Lastly,a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads.Furthermore,an operation and maintenance(O&M)knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.

Application of Data mining in Enterprise Network Marketing

With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise network marketing has become the new darling of many business managers.The marketing data will become a key to develop a corporate marketing strategy as an important basis tool. However, many companies now make mistakes in marketing data. They can＇t fully exploit the marketing data.lt can affect the development of enterprise network marketing strategy. This paper is based on the concept of marketing data and outline the importance of data mining for network marketing, then analyze a significant impact on the entemrise network marketin~ strate^w made by marketinR data mininR.

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.

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.

Analysis and Visualization of Marketing, Statistical and Macroeconomic Data With GIS

The most common way to analyze economics data is to use statistics software and spreadsheets.The paper presents opportunities of modern Geographical Information System （GIS） for analysis of marketing, statistical, and macroeconomic data. It considers existing tools and models and their applications in various sectors. The advantage is that the statistical data could be combined with geographic views, maps and also additional data derived from the GIS. As a result, a programming system is developed, using GIS for analysis of marketing, statistical, macroeconomic data, and risk assessment in real time and prevention. The system has been successfully implemented as web-based software application designed for use with a variety of hardware platforms （mobile devices, laptops, and desktop computers）. The software is mainly written in the programming language Python, which offers a better structure and supports for the development of large applications. Optimization of the analysis, visualization of macroeconomic, and statistical data by region for different business research are achieved. The system is designed with Geographical Information System for settlements in their respective countries and regions. Information system integration with external software packages for statistical calculations and analysis is implemented in order to share data analyzing, processing, and forecasting. Technologies and processes for loading data from different sources and tools for data analysis are developed. The successfully developed system allows implementation of qualitative data analysis.

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.

Research on the application of the data mining technology in the electronic commerce network marketing

With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid development of China＇s economy. Data mining technology is an advanced science and technology, which has important implications for the e-commerce data processing. Through the summary of the data mining technology, this article puts forward the application of data mining technology in electronic commerce, in order to better promote the development of electronic commerce.

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.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

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.

Indian stock market prediction using artificial neural networks on tick data

Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.