Big Data Analysis Methods Research Articles

Nociception: the roles of vitamin D

Objective: to carry out a systematic computer-assisted analysis of scientific literature on on the relationships between the effects of vitamin and nociception (including the impact of vitamin D deficiency on the development of pain syndrome.Material and methods. For the query “(pain OR nociception) AND (vitamin D OR cholecalciferol OR VITD OR CALCITRIOL OR HYDROXYVITAMIN OR DIHYDROXYVITAMIN OR ALPHACALCIDOL)", 2318 references, including 77 references on alpha-calcidol, were found in the biomedical publications database PubMed. The computer-assisted analysis of this array of publications was carried out using the current big data analysis methods developed within the topological and metric approaches to recognition/classification problems.Result and discussion. A systematic computer-assisted text analysis of 2318 studies on the relationships between nociception and vitamin D showed that the analgesic effects of the vitamin are by anti-inflammatory action and the regulation of serotonergic, dopaminergic, and opioidergic neurotransmission. The lower blood levels of the major vitamin D metabolite 25 (OH)D is a risk factor for decreased pain threshold in myalgia, fibromyalgia, arthralgia, dorsalgia, and a number of neuralgias.Conclusion. In patients with impaired renal conversion of active vitamin D metabolites (especially in the elderly), the efficacy of cholecalciferol-based drugs is reduced, which makes it necessary to use the active forms of vitamin D.

E-Commerce Enterprise Supply Chain Cost Control under the Background of Big Data

Since the twentieth century, it has been an era of rapid development of information technology; the scale of data is almost the growth rate of the blowout type; no matter what it is, a large number of enterprises or departments are increasing a large number of cost data. However, the current cost management model still remains in the traditional management method and lacks a smarter big data analysis method. In addition, there is a lot of research on big data applications, and there are few e-commerce supply chains. Therefore, the research purpose of this study is to use big data technology to explore a series of practical operation methods for supply chain Cultural Communication Enterprises and summarize the operation mode of building SCC control by using big data technology. In terms of research methods, this study combined bibliographic review and empirical analysis, explored cost-based mobile e-commerce (EU) cost control related to big data information, used smart and digital analysis methods to thoroughly analyze CCE business issues from internal and external supply chains, established an e-commerce business supply chain cost control model based on big data technology and elaborated cost control procedures and measures. Finally, it summarized the research results and drew conclusions to provide a theoretical basis for promoting enterprises products products to reduce supply chain costs. The research in this study has achieved a breakthrough in the cost management and control of EE; it provides empirical guidance and theoretical reference for EE to adopt big data technology for cost command of supply chain (CCSC), could help EE to reduce cost of supply chain management to gain higher profit margins, and promote e-commerce industry as a whole to the next level eventually. This study concluded that the use of big data technology for cost command can solve a series of problems effectively, such as the lack of systematic analysis of cost, the lack of contractual partners, the serious waste of sales links, and the policy errors of logistics links, and continuously improve the enterprise management level and the decline of comprehensive cost. The application mode of supply chain CCE enterprises using big data technology constructed in this study has universal applicability.

Comparison of Main Algorithms in Big Data Analysis of Telecom Customer Retention

In today’s fierce telecommunications market competition, customer chum is very severe. In order to retain customers, telecommunications companies have made various attempts from various data and consumption characteristics analysis to big data analysis. However, since the actual situation of customer churn is very complicated, how to predict customer churn accurately and quickly is a difficult problem. After the researchers successfully conducted big data analysis of customer churn and successfully retained customers, in this article, the researchers mainly compared several commonly used algorithms in order to find a better algorithm for big data analysis of telecommunications customer churn. Compare and analyze the accuracy and efficiency of these several algorithms and suggest that the business support staffs of telecommunications companies adopt major methods for big data analysis. The researcher found that the Decision Tree (CART) algorithm is better for the prediction of customer churn and guided other branch staffs to predict customer churn and retain customers in a timely manner. This kind of big data analysis can be used to retain customers in the telecommunications industry.

Research on Automatic Technology of New Urban Sewage Inspection and Treatment

The automatic control technology is more and more widely used in sewage treatment systems. The thesis elaborated on the new urban sewage treatment technology and control process, analyzed the PLC-based sewage treatment control system structure, realized the control of power equipment and testing equipment, and finally completed the remote monitoring of the SBR sewage treatment based on MCGS. Simultaneously, the paper uses big data analysis and statistical methods to analyse and compare the routine monitoring and important monitoring data of sewage treatment, effectively saving monitoring, feedback, improvement, and release time. The actual operation results show that the system is stable and reliable and can remotely monitor and control equipment operation in real-time.

The Construction of Big Data Platform for National Water Resources Monitoring Project

With the improvement of national informatization level, big data method is applied to more and more industries and fields, While the traditional water conservancy industry rarely uses big data technology and ideas to solve problems. The national water resources monitoring capacity building project is a major project es-tablished in order to implement the most stringent water resources management system in China. The project es-pecially emphasizes the integration and sharing of data and the application of big data technology, and builds a water resources big data platform. The water resources big data platform builds a theme database of water re-sources industry, and uses the big data analysis method to analyze the total water resources The practice shows that the platform not only provides decision support for the Ministry of water resources, but also improves the emer-gency management ability of water conservancy department.

Analyze the Development of Public English Courses education in Vocational schools Based on AI and Big Data

Under the influence of economic globalization, more and more technical talents with good English expression ability are needed in modern society. At present, public English has become a compulsory course in vocational school. In order to train talents loved by enterprises, vocational school organize various education activities that are conducive to public English Learning under the background of national education reform. The purpose of this paper is to analyze the development of public English Courses education in Vocational schools Based on AI big data. This paper summarizes the latest research progress of big data analysis method based on AI technology. This paper analyzes the research status of machine learning in big data environment. Then this paper proposes a personalized and developmental curriculum education model. The model is based on the big data composed of classroom education, extracurricular self-study and part of daily life. Through data mining technology, the evaluation of each student in the course of learning is given. The results show that the failure rate and the average rate of the class are greatly reduced, and the good rate is increased from 30% to 66%.

METHODICAL APPROACH TO THE QUANTIFICATION OF ENTERPRISE FINANCIAL SECURITY LEVEL

The method of evaluation of the level of an enterprise financial security as a priority criterion of its economic activity is proposed in the article. It is established that the calculation of indices for evaluation the financial security of a company is mainly based on the financial statements. According to these financial documents, one or another factor is determined, from the ratio of which the indices of an enterprise financial condition are formed as the information basis for the quantification of the level of its financial security
 It is proved that in the process of quantitative evaluation of an enterprise financial security level, it is necessary to use the methods of big data analysis. It is proposed to use the Monte Carlo simulation method as the most adaptive to such problems and common among the methods of quantitative estimation of economic processes, the model of which is based on the generation of values consisting of random variables, over which a series of experiments are conducted in order to detect the influence of the initial data on the dependent variables. It is substantiated that the use of the Monte Carlo method allows not only to determine the probability of a future level of an enterprise financial security, but also to obtain the assessment of the deviation risk of certain indices of the financial condition from the established limits of normative values and to identify possible threats to the financial security of an enterprise.
 It is proved that the analysis of the results of quantitative evaluation of an enterprise financial security level is the basis for making sound management decisions on optimization its financial risks and achievement the financial stability of an enterprise

A method for extracting travel patterns using data polishing

With recent developments in ICT, the interest in using large amounts of accumulated data for traffic policy planning has increased significantly. In recent years, data polishing has been proposed as a new method of big data analysis. Data polishing is a graphical clustering method, which can be used to extract patterns that are similar or related to each other by identifying the cluster structures present in the data. The purpose of this study is to identify the travel patterns of railway passengers by applying data polishing to smart card data collected in the Kagawa Prefecture, Japan. To this end, we consider 9,008,709 data points collected over a period of 15 months, ranging from December 1st, 2013 to February 28th, 2015. This dataset includes various types of information, including trip histories and types of passengers. This study implements data polishing to cluster 4,667,520 combinations of information regarding individual rides in terms of the day of the week, the time of the day, passenger types, and origin and destination stations. Via the analysis, 127 characteristic travel patterns are identified in aggregate.

RETRACTED: Business big data analysis based on microprocessor system and mathematical modeling

Big data analysis requires examining large amounts of data. This is, so discover the hidden patterns, and give the insight to make decisions on the correct business. Companies, because it uses the power of data and technology be a more objective data-driven. Big data of human resources, faced with various problems from big data storage security problems. These issues have been discussed in the next four categories based on the Contextual Data Analyzing Technique (CDAT) using FPGA tool. Data were collected and finally to analysts that are eligible to determine the problems caused by direct analysis, on security issues, and to find the problem. Companies that big data is presented to improve the company's strategy, to the best of time, various opportunities to make the right decisions. Human resources are one of the substantive departments of any company that needs big data benefits in a different process. The main purpose is to describe the big human data analysis method to improve company performance. Also, the problem is, all of the human resources department is that it is facing using big data.

Big data analysis and prediction method of multidimensional space-time features of energy internet based on fuzzy rough model

Nowadays, energy has become a hot issue of concern to the whole society. With the unbalanced distribution of resources in the world and more severe climate change, the constraints of resource conditions and environmental status on global energy development are becoming stronger and stronger. The rapid development of the Internet, as well as the proposal of the energy Internet, has a better application in the analysis of energy demand, which can effectively alleviate the contradiction between energy and environment. Aiming at the big data of energy Internet and based on the advantages of fuzzy rough model, this paper studies a method of big data analysis and prediction of multidimensional space-time characteristics of energy Internet based on fuzzy rough model. Firstly, according to the spatio-temporal characteristics of energy Internet data, extract the multidimensional spatio-temporal characteristics of energy internet. Secondly, rough set and fuzzy set are two commonly used mathematical tools, and the combination of the two fuzzy rough models can more fully mine data information. In view of the shortcomings of the commonly used fuzzy rough set reduction algorithm, a reduction algorithm based on conditional entropy is proposed. Finally, taking multidimensional space-time characteristics as input, combining the advantages of fuzzy rough model and neural network, a prediction model is established to analyze and forecast energy demand. The simulation experiments show that the design method is feasible and superior, and can achieve the prediction of energy demand well, so as to make more rational use of energy.

Causes and Analysis of the High Turnover Rate of Counselors in Private Higher Vocational and Technical Colleges in China

Counselors in China Higher Vocational and Technical College are mainly responsible for educating and guiding students' behavior and moral standards, correcting some students' behavior and strengthening students' legal consciousness. Counselors are good teachers and helpful friends of students' quality education. The characteristics of counselors as important educators cannot be ignored. But in reality, the assessment, evaluation, promotion, salary adjustment and other aspects of higher vocational colleges still favor front-line teachers and researchers, while the counselors are ignored. This practice has damaged the professional confidence and enthusiasm of counselors, which is an important reason for the high dimission rate of counselors, which has aroused the attention of many educators. In this context, as early as 1999, there appeared a lot of academic papers on the analysis and research of the high dimission rate of counselors in private vocational colleges. The high turnover rate of counselors in private higher vocational and technical colleges in China has seriously affected the teaching quality of the schools, as well as the students' behavior and moral norms during the school period. The purpose of writing this article is to study how to solve the problem of high dimission rate of counselors in private vocational colleges through detailed data and systematic demonstration. At the same time, it is scientifically demonstrated that as the director of students' quality-oriented education, the counselor has the characteristics of the implementer of education. Using the method of big data analysis, we come to the conclusion that counselors are important implementers of students' quality-oriented education,and counselors also play an important role in cultivating students' good moral quality. This point of view is affirmed in the article, the counselor's position is irreplaceable.

A review on the methods for big data analysis in agriculture

The integration of information and communication technologies into agriculture lead to the development of precision agriculture. Nowadays it relies on Internet of Things devices, geospatial data, historical and real-time information, which has the potential to transform farming into smart farming. However the use of Big Data requires significantly different skills and knowledge, compared to what many farmers and agronomists possess, which is an obstacle for their effective use. This study aims to summarize and provide insight into the common methods used for data analysis in a wide variety of agricultural applications. Initially, the basic characteristics and sources of agricultural data are explained. Next, a review of the common data analysis methods (classification, clustering and regressions) is provided, containing information about the data sources used as well as the desired goal of the analysis. At the end of the paper, a summary is given on the applicability of data analysis methods depending on the desired goals.

Retracted] Effects of Attentional Withdrawal on Chinese Disabled Biathletes’ Shooting Performance

Under two situations of static empty gun preview and simulated competition, the validity of self‐suggestion language “preloading compaction,” which suggests attentional withdrawal, was verified, respectively. Methods. The experiments in the two parts were designed in a single factor group. The participants (Chinese disabled biathletes) fired 20 times (4 groups in total, 5 times in each group) both in pretest and posttest, and they used self‐suggestion before each group of firing in posttest. Optoelectronic shooting device based on wireless laser transceiver technology was selected to collect the shooting technical indicators, scores, and other information. The operation evaluation form reflected the subjective feelings of the participants, and the heart rate was measured by the chest band. Results. By the method of big data analysis, under the condition of static empty gun preview, the results showed that there was an extremely significant difference between shooting score and quality of triggering. All the marks on the operation evaluation form improved after the intervention. In the mock competition situation, shooting score has highly significant differences with quality of triggering and relative triggering value and significant difference with aiming ability. The marks of the operation evaluation form have been improved; the heart rate of firing all decreased in different degrees. Conclusions. The attentional withdrawal has positive effects on the shooting scores, technical indicators of optoelectronic shooting device, subjective feelings, and heart rate of disabled biathletes under the two conditions. TIRE (the timing of firing) is the most sensitive indicator and can be used as the focus of future training and scientific research.

A review of big data analytics on customer complaints in the electricity industry

Fulfilling customer satisfaction gives significant impact to any business including in the electricity industry. The key to achieving customer satisfaction is by providing them the best quality services at fair and reasonable costs. Customer complaints must be managed professionally and appropriately, and be leveraged to improve the quality of services and operational efficiency. In this regard, identifying the root cause of the problems becomes paramount in improving customer service for future improvement. The accumulated customer complaints generate massive data which can be fully utilised by using big data analytics. The purpose of this paper is to determine frequent customer complaint regarding electricity issue and review various methods of big data analytics that have been used to identify valuable insights within the data and to analyse the pattern that can be useful to find solutions to the problem, thus improving the electricity industry services especially in terms of complaint management. On the basis of a study of the different researches, different techniques of machine learning have been used because of its accuracy and in finding a pattern to solve the relevant electrical problem such as predicting power demand, managing power loads, and enhancing strategic planning.

Analysis of digital technologies in the agri-food sector based on big data analysis methods

Analysis of digital technologies in the agri-food sector based on big data analysis methods

The environmental protection path of world cultural heritage sites from the perspective of circular economy

This article aims to study the environmental protection issues of world cultural heritage sites from the perspective of circular economy. This article uses big data analysis methods, using surveys, interviews, statistics and other techniques to investigate people's understanding of the circular economy and the environmental protection of world cultural heritage sites. The results show that about 55% of college students have only a little understanding of circular economy, about 58% of college students believe that the environmental protection of world cultural heritage sites is very necessary; at the same time, 64.5% of people can fully realise the importance of environmental protection. In addition, most people believe that the environmental protection of world cultural heritage sites can effectively protect world cultural heritage, promote economic development, sustainable social development, and improve social civilisation.

Network security defence system based on artificial intelligence and big data technology

Communication network security is a defence against unwanted system modifications to access files and folders within a computer network. The increasing overlap between the physical and virtual realms of improved communication poses a problem of cybersecurity. In this research, the big data analytics-based security system (BDASS) has been proposed to improve the communication network's security defence system with artificial intelligence (AI). The big data sets representing multiple categories of data are used in big data analysis methods. AI offers algorithms that can think or learn and strengthen their behaviour. Automated systems currently are built on syntactic rules that are not necessarily sufficiently sophisticated to handle the degree of difficulty in the communication network system. The BDASS model achieves a less computation time ratio of 21.3%, misbehaviour detection ratio of 97.5%, attack prediction accuracy ratio of 95.6%, possibility ratio of 96.4%, and success rate of 98.7% compared to other methods.

Data Analysis of Autism Disorder and Micronutrition using PubMed Abstracts

Aim: This study was conducted to identify the notable nutrients mentioned in abstracts of articles about autism disorder using the big data analysis method. Methods and Materials: Abstracts of articles on nutrients related to autism disorder were extracted from MEDLINE using PubMed. Text mining was performed to extract nutrient-related words from collected research abstracts. Network analysis was performed using nutrients keywords and showed more than 100 occurrences among extracted words. Results: As a result of text mining 932 abstracts, the top 10 Nutrients that appeared more than 100 times were vitamin D omega 3, zinc, iron, copper, vitamin B6, vitamin B12, folate, calcium, and probiotics, in decreasing order. Folate and copper were central nutrients in the central analysis of the network made by 10 nutrients. Grouping the 10 nutrients showed folic acid, vitamin B6, vitamin B12, calcium, iron, zinc, copper, omega 3 in group 1, probiotics in group 2, and vitamin D in group 3. Conclusions: A variety of nutrients, including vitamin D, folate, and copper, were found to be related to autism disorder by big data analysis of abstracts of nutrition research studies related to this disorder.

Big Data Analysis Methods Based on Machine Learning to Ensure Information Security

In this article the methods of data mining which is the basis of modern search algorithms are considered. The classification of machine learning methods is carried out and the principal difference between them is shown. Mathematical aspects of object classification are described. Possibilities of application of algorithms of machine learning for protection of information systems are considered. The concept of building a new method of primary information analysis is offered. Theoretical bases of machine learning and image recognition in conditions of incomplete a priori information about classes are considered and systematized. The scheme of practical application of theoretical classification algorithms based on real input data is shown. The result of the research showed the possibility of using machine learning technologies for data protection with the help of data mining to reveal hidden embedded information in the object of research.

Demand Analysis of Online Chinese Behavior Expression in Wireless Sensor Network

Due to the common progress and interdependence of wireless sensor networks and language, Chinese semantic analysis under wireless sensor networks has become more and more important. Although there are many research results on wireless networks and Chinese semantics, there are few researches on the influence and relationship between them. Wireless sensor networks have strong application relevance, and the key technologies that need to be solved are also different for different application backgrounds. In order to reveal the basic laws and development trends of online Chinese semantic behavior expression in the context of wireless sensor networks, this paper adopts big data analysis methods and semantic model analysis methods and constructs semantic analysis models through PLSA method calculations, so that the λ construction process conforms to this research topic. Research the accuracy and applicability of the semantic analysis model. Through word extraction of 1.05 million word data of 1,103 documents on Baidu Tieba, HowNet, and citeulike websites, the data set was integrated into a data set, and the PLSA model was verified with this data set. In addition, through the construction of the wireless sensor network, the semantic analysis results in the expression of Chinese behavior are obtained. The results show that the accuracy of the data set extracted from 1103 documents increases with the increase of the number of documents. Second, after using the PLSA model to perform semantic analysis on the data set, the accuracy of the data set is improved. Compared with traditional semantic analysis, the model and the big data analysis framework have obvious advantages. With the continuous development of Internet big data, the big data methods used to count Chinese semantics are also constantly updated, and their efficiency is constantly improving. These updated semantic analysis models and statistical methods are constantly eliminating the uncertainty of modern online Chinese. The basic laws and development trends of statistical Chinese semantics also provide new application scenarios for online Chinese behavior. It also laid a ladder for subsequent scholars.