Big Data Mining Technology Research Articles

Research on Innovative Application of Artistic Characteristics of Building Decoration Materials in Interior Design Based on Big Data

In the 21st century, people have entered the era of big data. People’s life is inseparable from the comparison of big data mining technology, including Building Decoration Materials (hereinafter referred to as BDM). Through big data technology, we can select more suitable BDM, which can improve people’s artistic expression level. The artistic characteristics of decorative materials can show a certain artistic beauty, which fully shows people’s artistic wisdom and level. However, with the continuous improvement of appreciation level, the traditional method of strengthening the indoor aesthetic feeling only by the artistic characteristics of decorative materials has failed to meet the requirements of people. Therefore, it is necessary to fully combine the visual experience of decorative materials to improve the artistic characteristics. This paper analyzes the decorative materials in color, texture, texture, specifications and other aspects of visual performance, which can better enhance the artistic characteristics of Interior design (hereinafter referred to as ID). Finally, this paper puts forward some practical cases.

Research on the Promotion Path of Teachers’ Scientific Research and Innovation Ability based on Big Data Analysis of “Double High Program” Construction

Under the national vocational education reform, HVC (hereinafter referred to as HVC) begin to implement the “double high program”, which is an important measure to promote the rapid development of vocational education. Among them, the improvement of teachers’ scientific research and innovation ability is an important part of the “double high program”, which is a solid foundation for the quality of personnel training. The 21st century is the era of big data, which requires HVC to improve teachers’ scientific research and innovation ability based on big data analysis. At present, there are many problems in HVC, which need us to improve teachers’ comprehensive ability. Firstly, this paper analyzes the basic principle of big data mining technology. Then, it puts forward the factors that restrict the development of teachers’ scientific research and innovation ability. Finally, this paper puts forward some suggestions, which can better build a higher vocational college research and innovation ability promotion system.

Research on anthrax toxin analysis based on big data mining technology

Anthrax is still endemic in some areas around the world, and a large number of people and livestock are at risk of contracting anthrax. As the pathogenic substance of Bacillus anthracis, anthrax toxin plays an essential pathogenic role. Researchers have conducted detailed studies and reports on their genes, structures, and biological activities. However, few articles analyse the field from the perspective of bibliometrics. In the present study, we used bibliometric software such as Citespace to examine the literature data and aimed to provide a reference for researchers in this field. In terms of journals, P NATL ACAD SCI USA, INFECT IMMUN and J BIOL CHEM rank among the top 10 as to the number of published articles and the number of co-citations, and they have an important influence in the field of anthrax toxins. As for researchers, the quantity and quality of the articles published by the three researchers, LEPPLA SH, MOAYERI M, and COLLIER RJ, have advantages. Their research on anthrax toxin is worthy of attention. On the frontiers of research, future research may have various directions, such as the in-depth explanation of the mechanism of anthrax toxin-induced diseases, how to use anthrax toxin to enhance anti-tumour methods to guide the development of new vaccines based on the immunological mechanism of anthrax toxin, and use new materials and technologies to detect different aspects of development of anthrax toxins, etc.

Pop Music Trend and Image Analysis Based on Big Data Technology.

With people's pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer's songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

Big data mining based coordinated control discrete algorithm of independent micro grid with PV and energy

The independent micro-grid with photovoltaic and energy storage has the problems of unknown line resistance, large load fluctuation, large control voltage error and poor application stability. In order to solve these problems, big data mining technology is applied to the design of control algorithm, which helps to achieve the coordinated control of the independent micro-grid. Firstly, the discrete consistency controller is constructed as the implementation medium of the control algorithm, and the photovoltaic cell, energy storage unit and DC-DC converter are combined to obtain the independent micro-grid model. In this model, mining the data of the independent micro-grid, and the micro-grid operation mode is determined by the data. According to different operation modes, the goal and constraints of the coordinated control are set, and the coordinated control mode is selected. The coordinated control of the independent micro-grid with PV and energy storage is realized from three aspects: photovoltaic unit, energy storage unit and operation mode switching. Through the comparison with the traditional control algorithm, it is concluded that under the grid connected condition, the control error of the grid voltage can be effectively reduced by applying the designed control discrete algorithm, and the control error can be reduced by 2 V under the island condition.

Retracted] Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

The purpose is to enrich the evaluation system of physical education (PE) teaching in colleges and universities and to improve PE teaching methods and improve teaching quality. Based on big data information fusion and data mining technology, firstly, the related theories of teaching evaluation are analyzed and expounded, as well as the characteristics and principles of the construction of college PE teaching evaluation system. Secondly, from the perspective of evaluation index system of sports teachers’ teaching and students’ sports teaching, the content and evaluation index of college sports teaching evaluation are analyzed under the background of big data information fusion and data mining by questionnaire survey. Combined with model test, the results show that traditional college sports teacher pays more attention to the design and teaching methods of PE and ignore the learning process of students. The evaluation process of PE ignores the individual differences of students, the feedback method lacks openness, and the evaluation process is isolated. Based on the big data technology and teaching evaluation theory, the evaluation index is designed for PE teaching in colleges and universities. The average value of the first layer indexes is above 4, and the coefficient of variation is less than 0.2, which can basically reflect the content of PE teaching evaluation and provide some reference for the research of PE teaching evaluation.

Predicting the ratings of Amazon products using Big Data

AbstractThis paper aims to apply several machine learning (ML) models to the massive dataset present in the area of e‐commerce from Amazon to analyze and predict ratings and to recommend products. For this purpose, we have used both traditional and Big Data algorithms. As the Amazon product review dataset is large, we present Big Data architecture suitable massive dataset for storing and computation, which is not possible with the traditional architecture. Furthermore, the dataset contains 15 attributes and has about 7 million records. With the dataset, we develop several models in Oracle Big Data and Azure Cloud Computing services to predict the review rating and recommendation for the items at Amazon. We present a comparative conclusion in terms of the accuracy as well as the efficiency with Spark ML—the Big Data architecture, and Azure ML—the traditional architecture.This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Technologies > Prediction

Characterizing Tourism Destination Image Using Photos’ Visual Content

“A picture is worth a thousand words”. Analysis of the visual content of tourist photos is an effective way to explore the image of tourist destinations. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by convolutional neural network (CNN) approaches breaks through the limitations of manual approaches of identifying photos’ visual information, e.g., small sample size, complex identification process, and results deviation. In this study, 531,629 travel photos of Jiangxi were identified as 365 scenes through deep learning technology. Through the latent Dirichlet allocation (LDA) model, five major tourism topics are found and visualized by map. Then, we explored the spatial and temporal distribution characteristics of different tourism scenes based on hot spot analysis technology and the seasonal evaluation index. Our research shows that the visual content mining on travel photos makes it possible to understand the tourism destination image and to reveal the temporal and spatial heterogeneity of the image, thereby providing an important reference for tourism marketing.

Smart Education — The Necessity And Prospect Of Big Data Mining And Artificial Intelligence Technology In Art Education

smart education refers to a new education form and education mode relying on Internet technology, big data mining, artificial intelligence (AI) technology and mobile communication technology to realize online, platform, convenient, modular, accurate, and even mobile and fragmented learning knowledge and skills. This is a big trend. The education form of traditional art colleges is old-fashioned, conservative and lack of innovative means to adapt to it. It is imperative to improve the guidance, systematicness and convenience of art education curriculum by using new technology and new means. However, it is not very beneficial to simply transfer the curriculum and teaching materials under the offline learning mode to the Internet for all art students. Therefore, we put forward the concept and Prospect of "smart education". In short, smart education is the comprehensive development and utilization of online education platform, knowledge base, artificial intelligence (AI) and big data mining technology.

Research on the Development and Application of Computer Data Mining Technology

With the rapid development of computer, data mining technology has become a new technology, which can promote the development of computer. By promoting the progress of science and technology, mining technology can meet the search needs of people’s lives, which has made great contributions to the whole society. Through computer data mining technology, we can promote the good development of the whole computer field, which solves the specific needs of people for big data information. First of all, this paper analyzes the development process of computer data mining technology. Then, this paper analyzes the development algorithm of computer data mining technology. Finally, this paper proposes a development model based on big data mining technology.

Analysis of college students' consumption behavior based on campus card data

In order to further improve the intelligent management level of universities, we use big data and data mining technology to analyze the data including a university's campus card consumption records. Firstly, through the preprocessing and feature engineering of the extracted data, we analyze students' consumption level, consumption habits and other laws. Then, we cluster students into different groups with different consumption behaviors, and analyze the consumption structure and consumption behavior characteristics for the different groups in depth. Finally, by constructing a student co-occurrence network, we study the social relationships of students and find the lonely persons. The experimental results show that our clustering algorithm can assist the accurate student aid work, and the construction of the student co-occurrence network can also provide a certain reference for the psychological counseling work.

Design of cancer classification and visualization platform based on Internet big data

With the development of Internet technology, Internet technology is widely used in the medical industry. Based on the medical Internet big data, using visualization, data mining and other technologies, combined with distributed crawler, HTTP data transmission technology, vue2 front-end framework and the application of visualization technology, this paper completed the development and design of the analysis platform based on the internet medical big data. From the three aspects of presentation layer, business logic layer and data access layer, the architecture can show the number of hospitals, the proportion of doctors, the number of departments, the hospital’s praise, poor evaluation and other information. Then, the cancer big data classification model based on the new bionic optimization algorithm is established. The experimental results show that the method has high classification accuracy and speed. In the era of big data, the integration of early diagnosis and treatment of cancer resources and information construction is very necessary and has far-reaching significance.

The Perception Difference Analysis of the Influence of Coastal Residents of Big Data Mining Technology on Marine Tourism Development

Yan, S.L., 2020. The perception difference analysis of the influence of coastal residents of big data mining technology on marine tourism development. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 265-267. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of social economy in China, tourism as a major emerging industry gradually began to grow. As a choice direction of tourism, marine tourism has not only made great contributions to driving local economy and coordinating the comprehensive development of other industries, but also promoted the overall development of coastal cities. According to big data mining technology, this paper conceive a general model suitable for marine tourism development, and promote the healthy, stable and green development of marine tourism. Marine tourism has developed into the core industry of marine economy, and the future development prospects are bright. However, in the face of the blind exploitation of marine resources, it has caused serious damage to marine ecology. If the marine ecology can not be repaired and protected in time, the impact on marine tourism development will be devastating. Marine tourism is a brand new industry. After years of development, it has become the pillar of marine economy, and the future development space is huge. In recent years, the number of tourists received by the marine tourism industry has shown an upward trend every year, which also makes marine tourism face severe challenges. Therefore, digging deep into the development potential of marine tourism resources, deepening the quality and management level of tourism services, is an important way to meet the actual needs of tourists, optimize the existing industrial structure of tourism, and is also an important direction for the sustainable development of marine tourism industry.

Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market

In recent years, China's bond market has experienced rapid development, but the pace of credit risk supervision has not kept up. Since 2014, the number of domestic credit bond defaults has increased. In 2016, there were 79 domestic default bonds, with a default amount of up to 40.3 billion Yuan. From the perspective of domestic bond market credit risk supervision and early warning mechanism, rating is not objective, and tracking is not timely also rating methods are backward. Therefore, with the development of big data and other technologies, it is urgent to study credit risk supervision methods suitable for the domestic bond market. On the basis of combing the development of domestic bond market and analyzing the current situation of domestic credit rating, this paper combines the results of theoretical research at home and abroad, the information available in the domestic market, big data mining and automation technology, based on the financial and stock exchange information of listed companies, combined with BS option pricing theory, constructs KMV model.

Knowledge discovery from remote sensing images: A review

AbstractThe development of Earth observation (EO) technology has made the volume of remote sensing data archiving continually larger, but the knowledge hidden in massive remote sensing images has not been fully exploited. Through in‐depth research on the artificial intelligence (AI)‐based knowledge discovery approaches from remote sensing images, we divided them into four typical types according to their development stage, including rule‐based approaches, data‐driven approaches, reinforcement learning approaches, and ensemble methods. The basic principles, typical applications, advantages, and disadvantages have been detailed for commonly used algorithms within each category. Conclusions include the following: (a) Rule‐based, data‐driven and reinforcement learning algorithms form a trilogy from knowledge to data, and to capabilities. (b) Rule‐based data mining algorithms can provide prior knowledge for data‐driven approaches, the knowledge discovered by data‐driven models can be as an important complement to expert knowledge and rule sets, and reinforcement learning approaches can effectively make up for the lack of training samples or small training sample in data‐driven models. (c) The traditional data‐driven machine learning approaches and their ensemble methods are the current and may be the future mainstream methods for large regional and even global scale long time series remote sensing data mining and analysis, and improving their computing efficiency is the key research direction. (d) Deep learning, deep reinforcement learning, transfer learning, and an ensemble approach of the three may be the main means for small‐area scope, short time series, and key geoscience information extraction from remote sensing images within a long time of the future.This article is categorized under:Algorithmic Development > Spatial and Temporal Data MiningFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Artificial Intelligence

Research and Application on Spark Clustering Algorithm in Campus Big Data Analysis

Big data analysis has penetrated into all fields of society and has brought about profound changes. However, there is relatively little research on big data supporting student management regarding college and university’s big data. Taking the student card information as the research sample, using spark big data mining technology and K-Means clustering algorithm, taking scholarship evaluation as an example, the big data is analyzed. Data includes analysis of students’ daily behavior from multiple dimensions, and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism, votes of teachers and students, etc. At the same time, students’ absenteeism, physical health and psychological status in advance can be predicted, which makes student management work more active, accurate and effective.

Network Big Data Analysis and Mining Based on DPI Technology

In recent years, the rapid development of mobile data network, with 4G commercial wireless network has entered the data era, more and more big data impact on the network of mobile operators, network monitoring and network security need to be strengthened. Depth packet detection technology DPI. The purpose of this paper is to complete network data analysis by in-depth identification of key words in IP packets, so as to meet the current demand of operators for network analysis. In this paper, DPI technology is applied to the actual network, and the DPI data analysis system is constructed by combining the 2G and 3G networks of China mobile. The key technologies in DPI include character recognition, behavior pattern recognition and application layer gateway recognition. At the end of the experiment, the mass signaling data from GB interface and iu-ps interface were obtained to effectively identify various applications and data mining in the network. This paper expounds big data analysis and mining technology and its decision application research from different aspects, hoping to provide theoretical reference for experts and scholars studying the application of big data analysis and mining technology.

Study on application of open source intelligence from social media in the military

With the gradual popularization of information technology and network technology, various types of open source information, especially social media information, have shown an explosive growth trend. Social media intelligence not only possesses ideological and value-level values, but also directly serves military defense. It will help stimulate open source intelligence to play a huge role in combat operations that combining communications and psychological knowledge to explore and exploit frontier technologies in artificial intelligence, such as machine learning and data mining, and conducting research on social media big data collection, analysis, mining, and prediction technologies. This paper focuses on the preliminary analysis of the application of open source information such as social media in the field of military intelligence.

A Random Classified-Storage Picking Path Model for V-Type Storage Layout

This paper studies a V-type layout design, establishes the area utilization model of a V-type layout based on big data technology, and verifies the validity of the area model. This paper studies the ABC classification and storage strategy of V-type layout and establishes a random model of return-shape picking paths for V-type layout. By calculating the sum of the expected picking distance in the main channel and the expected picking distance of the subchannel, a mathematical model for return-shape picking paths of the V-type layout is established. By using big data mining technology, this paper simulates a random picking path model and obtains simulated data for cases with multiple orders, providing a theoretical basis for research on random picking path models with a classified-storage strategy using an improved layout.

Research on Weibo user behavior system for subjective perception and big data mining technology

Research on Weibo user behavior system for subjective perception and big data mining technology