Development Of Big Data Technology Research Articles

A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data

The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density-based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real-timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real-time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources.

Teaching Mode Design and Effect Evaluation Method of Visual Communication Design Course from the Perspective of Big Data

Traditional visual communication design teaching courses only use books and courseware for teaching, which greatly limits students understanding and perception of visual communication design art courses. The visual communication design course is different from the teaching of other subjects; it requires students to have a deep understanding of the content of the video and the experience of emotion. Relying on courseware and book teaching methods will not only limit students’ imagination, but also make students tired of learning psychological activities. The teaching of visual communication design technology is to transmit the images, colors, words, and emotions of artworks to students in the form of video, which is a relatively abstract course. With the development of big data technology, this brings new opportunities for the teaching of visual communication design technology courses. In this study, data mining technology will be used to evaluate the effect of visual communication design teaching. And CNN will be used to predict the content characteristics of visual communication design teaching, which is part of the visual communication design teaching system. The research results show that the big data method has better performance in the visual communication design technology course; both the classification and prediction errors are within the acceptable range for the artwork of the visual communication design course. Big data technology can well predict the relevant features in visual communication design; the largest prediction error is only 2.66%, and the smallest error is only 1.21%.

An Intrusion Detection Model Based on Improved ACGAN in Big Data Environment

With the development of big data technology, network intrusion problems against server vulnerabilities emerge one after another. To improve the accuracy of intrusion detection, this paper designs an intrusion detection platform based on the ACGAN (auxiliary classifier generative adversarial network) model in a big data environment. Firstly, by introducing a self-attention mechanism, the global characteristics of attack samples are extracted to improve the quality of generated samples. Then, by adding a gradient penalty, the model's convergence speed and training stability are improved. Finally, this method enhances and expands the attack samples and verifies the dataset. The experimental results show that compared with other comparison methods, the overall detection accuracy of this system is higher, and the false-positive rate and false-negative rate are lower.

Research on Prediction of News Public Opinion Guiding Power Based on Neural Network

The development of big data technology and the popularity of we-media platforms make the prediction of news public opinion more complicated, which means that the complexity and dynamic nature of news public opinion require higher accuracy of prediction. With the rapid popularization of we media technology, traditional single-model algorithm is difficult to effectively predict network public opinion under the current background. Therefore, this paper proposes an algorithm based IGA-RBF neural network to deal with the complicated news public opinion prediction. Firstly, the ARMA (autoregressive moving average model) prediction model is constructed and the BRF neural network is combined. Then IGA is introduced to optimize BRF neural network, and the column vector of output matrix of hidden layer is optimized globally. The algorithm uses k-means clustering to select parameters in RBF network. The experimental results demonstrate that the model algorithm makes up for the shortcomings of the single prediction algorithm, improves the accuracy of prediction, and has better prediction results of public opinion trends.

Expansion of expert activity in the development of Big Data technologies in the context of the fourth industrial revolution

The application of expertise in various spheres of human activity at the present stage of economic development and the forecast of the role and place of a person in expert activity in the context of the fourth industrial revolution are shown. Examples of determining the main actors and the degree of regulation of certain types of expert activity, such as forensic examination, audit, and appraisal activity, are given. Poor ontological and terminological elaboration of examinations in scientific and technical activities and at the level of corporate activities is indicated. With the further development of Big Data technologies, the activity to determine the compliance of events with the criteria and the calculation of all possible risks is assumed by artificial intelligence, and making a decision on the choice of a specific option (expert opinion) and, accordingly, taking responsibility for the consequences will become the key profession of a person of the future. The university, with its experience of working with human potential, becomes the core of the development of expert activity as the future profession of humanity.

Data Protection of Internet Enterprise Platforms in the Era of Big Data

With the development of big data technology, processed data has become an important source of value. Data has played a pivotal role in the development of enterprises, especially internet enterprises. However, Internet enterprise platform companies generally infringe on personal privacy in various stages of information collection, processing and application, and Internet enterprise platform data protection research is of great significance. The study found that the current problems of data protection on Internet enterprise platforms include: extremely weak user data protection measures, intellectual property risks throughout the whole process of big data processing, and infringements that have both new and high-tech characteristics. The high ambiguity in the definition and attribution of “data rights”, the low cost and high concealment of infringements, and the value difference between intellectual property protection and digital economy are the main causes of these problems. As far as the protection path is concerned, we should start from the three aspects of technology empowerment, governance empowerment and legal empowerment, and work together to promote the proper protection of Internet enterprise platform data.

Transformation of innovation activity in modern corporate management

Aim. The presented study aims to substantiate the priority directions of corporate innovative activity in modern management in the context of structural digital transformations of the global economic system, which lead to the reformatting of business models of economic entities in the long term.Tasks. To achieve the set aim, the authors comprehensively analyze theoretical and practical approaches to the forms and methods of transformation of innovation management in the modern economy, and propose recommendations on promising directions for the sustainable operation of companies that provide competitive advantages of business in a single network space.Methods. This study uses comprehensive analysis, methods of synthesis, induction, deduction, and expert research using information and communication technologies.Results. In the context of the digital economy, it is necessary to create a flexible organizational structure of companies, since the goal of the top management is not only to increase the capitalization of the business, but also to form an image of the brand using the methods of collaboration, strengthening of horizontal ties, and intellectual capital in the digital space. Modern management is focused on the intensive development of big data technologies and makes allowance for the increased role of precision work with clients, digital marketing, and artificial intelligence. Justification of the IT strategy, correct description of business processes based on cloud computing, BPM and SBPM technologies, and development of open interfaces are key factors for the sustainable operation of innovative companies.Conclusions. The evolution of management under the influence of digital technologies has led to the reformatting of the companies' life cycle. In the long term, economic entities can achieve competitive advantages by developing innovative formats and the geography of customer presence in the network space. Cloud technologies, virtual space, e-commerce, web portals, and automated systems have a great effect on the correction of priorities of predicted scenarios for the development of business structures. Modern management is characterized by an increasing amount of investment of multinational corporations in the segments of technological chains with "mass demand". Internet games, e-commerce, centers for storing and processing information and analytical databases are the priority areas for the development of management. Intellectual capital, collection, storage, and processing speed of an array of statistical data have become a prerequisite for progressive economic growth. The transformation of the organizational forms of traditional business models has influenced the evolution of the role of companies, which are becoming an integral part of the digital ecosystem, since the ownership and processing of information and analytical data make it possible to ensure competitive advantages and to obtain the effect achieved by optimizing network interaction between economic entities. Companies on the core of digital ecosystems are adjusting their long-term development strategy under the influence of integrated management effects in a single network. The reformatting of management has occurred as a result of the development of new organizational forms of value creation within the ecosystem, which has reduced the consequences of external and internal risks of the world market using flexible and innovative forms of interaction between economic entities. In the digital economy, the high adaptability of network coordination and intersectoral interaction in innovation processes based on collective intellectual property has increased the effectiveness of management.

Research on Innovative Design of Product Packaging Based on Big Data Technology

Traditional product packaging design mainly relies on the designer's personal experience and intuition, but there are problems such as uncontrollable content and lack of knowledge and guidance in the field of product design. With the development of big data technology, product packaging design in the era of big data is carried out under the support of a large amount of real data, which has high predictability, success rate, and short development cycle. Compared with structured data, unstructured data such as text, images, and audio has a higher value. Among them, text big data and image big data have good application prospects in the field of product packaging design due to their easy data acquisition, mature processing technology, and simple operation. This paper proposes a combination of big data technology and neural style transfer model and proposes an innovative design method for product packaging that can generate high-quality images and controllable content. First, the perceptual engineering theory is used to obtain user needs, to build a mapping model between product modeling elements and product semantics, and to guide the selection of product semantics and style maps; second, use neural style transfer models to reconstruct and combine the color features of style maps. After the integration, it migrated to product packaging design, based on big data product packaging innovation design methods, and developed a product innovation design auxiliary prototype system based on actual needs to improve the company’s R&D and innovation capabilities, shorten product development cycles, and reduce R&D costs. Improve product success rate and user satisfaction.

Analysis of Big Data Sharing Economy under Blockchain Technology

With the continuous development of big data technology, people have new development and pursuit in the fields of production, distribution, exchange, consumption and so on. During this period, the sharing economy came into being. The big data sharing economy refers to: relying on the support of big data, institutions or individuals with idle resources will pay others to use their own resources, and sharers create value by sharing their idle resources. With the support of blockchain technology, the big data sharing economy has been further developed, providing a new development idea and model for people's consumption. It has become a strong support for promoting the new development of the sharing economy and improving the business model of the sharing economy.

A novel oscillation identification method for grid-connected renewable energy based on big data technology

With the development of big data technology, power system has entered the era of data analysis. With the help of the massive data provided by the wide area measurement system, the power system can be easily evaluated, and the abnormal operation status can be detected and positioned. As the increase of renewable energy permeability, more new abnormal operating status have appeared in the system. Aimed at the abnormal operation state in the development of new energy, this paper proposes an oscillation location scheme based on evidence theory and support vector machine, which makes up for the limitation of single oscillation location method. The result of location analysis of oscillation energy method, oscillation phase difference method and forced oscillation phase difference location method is fused by evidence theory.

Data Ecology and Accurate Portrait: Optimization of Credit Risk System for SMEs in Supply Chain Finance Based on Big Data Technology

Big data technology can collect data, store it, mine it, and create an accurate portrait. It can assist financial institutions in resolving information asymmetry between banks and enterprises, as well as lowering the likelihood of default of small and medium-sized financing enterprises (SMEs). The credit risk system for SMEs in supply chain finance can realize “visualization” management of credit risk with the help of open public data in government affairs, collaborative development of various technologies, and the establishment of an ecological platform with transparent and accurate data portraits. The platform with accurate risk warning capability can reduce the risk monitoring cost and improve the risk management efficiency of financial institutions. The core enterprises are more willing to grant credit to SMEs through the big data technology supervision platform, which significantly improves the financing efficiency of SMEs. Moreover, a better financing credit circumstance also could improve transaction efficiency of enterprises and deeply connect the business relationship between enterprises. The main conclusion of this research: big data technology has a significant impact on supply chain in the digital economy era. Firstly, big data technology can identify credit risks accurately, which narrows the "information gap" between financial institutions and supply chain financing enterprises, and lower the likelihood of credit default. Secondly, financial institutions can allocate funds accurately based on the “visualization” information provided by the big data platform, and strengthen supervision of the use of funds. Lastly, the supply chain finance credit risk supervision system based on big data technology promotes the deep integration of big data and real economy. Therefore, in order to ensure the sustainable development of supply chain finance and financing risk management, it is necessary to create a digital ecosystem of supply chain finance with supply chain finance control tower as its core, as well as a supply chain finance credit risk control system based on big data in the context of the continuous development of big data technology.

Continuous Symmetry Analysis of the Effects of City Infrastructures on Invariant Metrics for House Market Volatilities

The invariant metrics of the effects of park size and distance to public transportation on housing value volatilities in Boston, Milwaukee, Taipei and Tokyo are investigated. They reveal a Cobb-Douglas-like behavior. The scale-invariant exponents corresponding to the percentage of a green area (a) are 7.4, 8.41, 14.1 and 15.5 for Boston, Milwaukee, Taipei and Tokyo, respectively, while the corresponding direct distances to the nearest metro station (d) are −5, −5.88, −10 and −10, for Boston, Milwaukee, Taipei and Tokyo, respectively. The multiphysics-based analysis provides a powerful approach for the symmetry characterization of market engineering. The scaling exponent ratio between park area percentages and distances to metro stations is approximately 3/2. The scaling exponent ratio expressed in the perceptual stimuli will remain invariant under group transformation. According to Stevens' power law, the perception-dependent feature spaces for parks and public transportation can be described as two- and three-dimensional conceptual spaces. Based on the prolongation structure of the Schrödinger equation, the SL(2, R) models are used to analyze the house-price volatilities. Consistent with Shepard’s law, the rotational group leads to a Gaussian pattern, exhibiting an extension of the special linear group structure by embedding SO(3) ⊗ R(3) in SL(2, R). The influencing factors related to cognitive functioning exhibit substantially different scale-invariant characteristics corresponding to the complexity of the socio-economic features. Accordingly, the contour shapes of the price volatilities obtained from the group-theoretical analysis not only corroborate the impact of the housing pricing estimation in these cities but also reveal the invariant features of their housing markets are faced with the forthcoming sustainable development of big data technologies and computational urban science research.

Parallel Algorithm of Improved FunkSVD Based on GPU

The increasing amount of network data and the development of big data technology have brought development opportunities and challenges to recommendation systems. Model-based collaborative filtering has become a mainstream algorithm in recommendation systems. For example, the Funk singular- value decomposition (FunkSVD) algorithm. However, in big data calculations, data sparseness and iterative oscillations often affect the accuracy of the FunkSVD algorithm. Moreover, when the data volume is in units of GB or more, the FunkSVD algorithm runs slowly and is ineffective. Therefore, we propose an improved FunkSVD algorithm (IFABG) based on the root-mean-square prop (RMSProp) and graphics processing unit (GPU) to solve this problem. First, we used RMSProp algorithm to improve the traditional FunkSVD algorithm, alleviate data sparseness and iterative shock, and improve the prediction accuracy of the algorithm. Next, we implemented parallelization of the improved FunkSVD algorithm in the GPU, which increased the calculation speed of the algorithm. Finally, we verify the IFABG algorithm using the Movielens dataset. The experimental results show that the IFABG algorithm is very suitable for processing sparse data, and it alleviates the iterative shock. The prediction accuracy rate is approximately 30% higher than that of the traditional FunkSVD algorithm. The experimental results also demonstrate that the IFABG algorithm has a good acceleration effect. For the same size dataset, the IFABG algorithm is faster than the traditional FunkSVD, and the acceleration ratio can be as high as 19.27.

Encoding Conversion Algorithm of Quaternary Triangular Mesh

In the process of global information construction, different fields have built their own discrete global grid systems (DGGS). With the development of big data technology, data exchange, integration, and update have gradually become a trend, as well as the associative integration of different DGGS. Due to the heterogeneity of DGGS and the different encoding rules, how to build the encoding conversion rules and data mapping relationship between the same object in various DGGS is an effective support and key technology to achieve the interoperability of DGGS. As a kind of multipurpose DGGS, the quaternary triangular mesh (QTM) has become an effective spatial framework for constructing the digital earth because of its simple structure. At present, there are many schemes for QTM encoding research, which plays a key role in the development of QTM, but at the same time, it also leads to difficulties in the communication and integration of QTM under different encoding. In order to solve this problem, we explore the characteristics of QTM encoding, and put forward three conversion algorithms: resampling conversion algorithm, hierarchical conversion algorithm, and row–column conversion algorithm.

Research on Face Recognition and Privacy in China-Based on Social Cognition and Cultural Psychology.

With the development of big data technology, the privacy concerns of face recognition have become the most critical social issue in the era of information sharing. Based on the perceived ease of use, perceived usefulness, social cognition, and cross-cultural aspects, this study analyses the privacy of face recognition and influencing factors. The study collected 518 questionnaires through the Internet, SPSS 25.0 was used to analyze the questionnaire data as well as evaluate the reliability of the data, and Cronbach’s alpha (α coefficient) was used to measure the data in this study. Our findings demonstrate that when users perceive the risk of their private information being disclosed through face recognition, they have greater privacy concerns. However, most users will still choose to provide personal information in exchange for the services and applications they need. Trust in technology and platforms can reduce users’ intention to put up guards against them. Users believe that face recognition platforms can create secure conditions for the use of face recognition technology, thus exhibiting a higher tendency to use such technology. Although perceived ease of use has no significant positive impact on the actual use of face recognition due to other external factors, such as accuracy and technology maturity, perceived usefulness still has a significant positive impact on the actual use of face recognition. These results enrich the literature on the application behavior of face recognition and play an important role in making better use of face recognition by social individuals, which not only facilitates their daily life but also does not disclose personal privacy information.

Differential Privacy in Practice: Use Cases

The problem of ensuring privacy is relevant in connection with the development of big data technologies. One of the modern and most promising methods of privacy protection is the differential privacy. In this paper the differential privacy applications developed by big companies are investigated. The libraries’ capabilities and tools of Google, IBM, as well as packages in R are analyzed. The differential privacy process for data collected from users implemented by Apple is studied.

Monitoring Methods of Marine Pollution Range Based on Big Data Technology

With the development of big data technology, traditional monitoring methods for the scope of marine pollution can no longer meet the current needs of accuracy and timeliness. In light of the outstanding topic, this study proposed to use big data technology to monitor the scope of marine pollution. The intelligent digital remote sensing technology was used for multi-dimensional monitoring of ocean water quality and completed the calculation of data collected by water quality sensors through the improved big data comparative analysis method. Finally, the scope of pollution monitoring was realized. The results verified that the proposed monitoring method could achieve high-precision and time-sensitive monitoring of the range of marine pollutants, and could identify the basic information of pollutants.

Fuzzy Neural Network Algorithm in Improving Electrical Engineering Control System

In the current era of rapid development of big data technology and artificial intelligence technology, China’s comprehensive national strength has also been significantly enhanced, the rapid progress of science and technology makes the electrical engineering control system whether in terms of efficiency and quality of the application, its electrical engineering control system development is gradually improved with the support of high-tech. Based on artificial intelligence technology, neural network algorithms and improved neural network algorithms are proposed to improve the original electrical engineering control system. In this paper, the S electrical engineering control system is the main research object, and its addition of fuzzy neural network algorithm to improve the study. Firstly, on the basis of a simple description of S electrical control system, the research status of the main partition control blocks of S electrical control system is analyzed. Secondly, an improved intelligent control system, including intelligent service interruption system and central electrical control system, is proposed to design an improved electrical engineering control system based on neural network algorithm through the operation principle of sensors and the study of network communication technology. Based on the above research basis, the effectiveness and practicality of the proposed intelligent electrical engineering control system are verified by analyzing the effects of the proposed intelligent electrical engineering control system in real life. The experimental results show that although there are still many problems in the intelligent control system of three-phase electrical engineering at this stage, innovation and technological progress will continuously improve the comprehensiveness and intelligence level of the system.

Structured Knowledge Base Q&A System Based on TorchServe Deployment

Structured tabular data are widely used in various information systems, especially with the development of big data technology, making it more difficult to query on these complex data. SQL facilitates the query on structured tabular, however, the mastery of SQL has a certain threshold for most non-expert users. Therefore, in order to facilitate ordinary users to quickly obtain the required information in complex structured data, we design and implement a Q&A system for structured knowledge. First, we make a detailed distinction between Q&A scenarios for structured data and design different approaches, respectively. Then, we introduce deep learning models in system algorithm layer to enhance the generalization ability. Finally, the TorchServe framework is used to optimize system deployment and improve system performance using batch inference. The experimental results show that the prototype system has certain generalization ability and also has some advantages in performance compared with traditional methods.

Big Data on Machine Learning – A Review

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.