The Impact of Big Data Technology on the Advancement of the Insurance Industry

This paper summarizes the current research background and application status of big data, systematically describes the domestic development of the concept and technology of big data, introduces the international advanced big data technology and the mainstream technology and methods of big data in China’s agricultural field, and the technology and application status of big data in the field of international cooperation; and discusses the existing problems. Finally, it is systematically proposed that the big data of international cooperation in agriculture should be applied to the research of international cooperation in agriculture, the application of big data in international cooperation in agriculture, and the application of big data in international cooperation in agriculture should form a solid triangular structure.

Under the era of big data, big data technology has been widely used in all aspects of insurance precision marketing, product design, risk control, internal operation and so on, which effectively solves the practical problems faced by insurance companies in the transformation and upgrading of insurance companies in the big data era, and it is conducive to promoting the high-quality development of the insurance industry. The new generation of information technology represented by big data technology has promoted the progress of insurance science and technology, infiltrating into all aspects of insurance marketing, products, pricing, service and management. On the basis of a brief description of the application of big data in the insurance industry, and taking the insurance pricing research as the starting point, this paper analyzes the positive and negative effects of big data's technology application on the insurance industry. Finally, it focuses on the specific research on the accurate pricing strategy of the insurance industry under the background of big data era from three aspects: establishing big data actuarial thinking, establishing accurate pricing model and insisting on customer-centered.

BackgroundAs COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China’s SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks.MethodsThis study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan’s application of big data technology in its COVID-19 epidemic emergency management.ResultsHainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose.ConclusionsThis study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

Under the big data(BD) technology, colleges and universities pay more and more attention to educational technology, so as to follow up with modern information, digitization, and visualization. The courses of civil engineering teaching with traditional teaching methods and single knowledge of students are profit-only, applying data mining(DM) technology and BD technology to realize positive thinking mode. This article first gives a brief overview of DM technology and BD technology, and then discusses the strategy of applying BD technology in teaching of civil engineering professional courses, and finally applies BD DM technology to the construction of civil engineering teaching system, to realize the intelligent teaching mode. Based on DM technology and BD technology, this paper comprehensively studies the construction of two technologies in the civil engineering teaching system of colleges and universities. This article combines the new concept of constructing attention to teaching, expounds the advantages of DM algorithm in the construction of civil engineering teaching system, and briefly analyzes the composition and development of the networked civil engineering teaching system platform. At the same time, this article also in-depth research In the classification algorithm of the DM algorithm, the k-nearest algorithm is discussed, and for the efficiency of their construction in the civil engineering teaching system, the visualization research is carried out from the operation efficiency of the algorithm steps and other aspects and the organization of the data set. Based on the above research, the optimization schemes of particle swarm algorithm and ant colony algorithm are designed, and a parallelized early warning system model is realized using GPU based on CUDA platform. In the above research, the two algorithms were compared and tested, and the efficiency of the algorithm in the construction of civil engineering teaching system was verified. Experimental research results show that the cosine correlation analysis based on DM algorithms and the hybrid particle swarm algorithm can effectively integrate BD technology to construct a civil engineering teaching system and improve the operating efficiency of the system.

PurposeThis paper aims to give an integrated framework for analysing the main opportunities and threats related to the exploitation of Big Data (BD) technologies within intellectual capital (IC) management.Design/methodology/approachBy means of a structured literature review (SLR) of the extant literature on BD and IC, the study identified distinctive opportunities and challenges of BD technologies and related them to the traditional dimensions of IC.FindingsThe advent of BD has not radically changed the risks and opportunities of IC management already highlighted in previous literature. However, it has significantly amplified their magnitude and the speed with which they manifest themselves. Thus, a revision of the traditional managerial solutions needed to face them is required.Research limitations/implicationsThe developed framework can contribute to academic discourse on BD and IC as a starting point to understanding how BD can be turned into intangible assets from a value creation perspective.Practical implicationsThe framework can also represent a useful decision-making tool for practitioners in identifying and evaluating the main opportunities and threats of an investment in BD technologies for IC management.Originality/valueThe paper responds to the call for more research on the integration of BD discourse in the fourth stage of IC research. It intends to improve this understanding of how BD technologies can be exploited to create value from an IC perspective, focussing not only on the potential of BD for creating value but also on the challenges that it poses to organizations.

This paper uses big data technologies to study base stations' behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, firstly we exploit big data platform and technologies to analyze cellular network big data, i.e. Call Detail Records (CDRs). Our CDRs data set, which includes more than 1000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Secondly, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations' behaviors and dissect their predictability. Thirdly, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations' activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations' behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.

This paper uses big data technologies to study base stations’ behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, first we exploit big data platform and technologies to analyze cellular network big data, i.e., Call Detail Records (CDRs). Our CDRs data set, which includes more than 1,000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Second, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations’ behaviors and dissect their predictability. Third, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations’ activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations’ behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.

Smart grid is one of the most important applications in big data. Mastering the application technology of smart grid and big data is of great significance for the sustainable development of the power industry and the establishment of a strong smart grid. This paper introduces the basic concepts of big data in smart grid, including data source, data characteristics and the domestic and foreign research status and application of smart grid data; It also analyzes key application areas of big data in smart grid and its value, summarizes the key technology of big data and the challenges it faced; finally, it puts forward the technology development route of big data in smart grid.

Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia

Design of Network Data Information Security Monitoring System Based on Big Data Technology

The new information and communication technologies have changed the way of teaching and learning. In particular, the big data technology that has recently been developed to overcome the limitations of traditional systems of storage, processing, and analysis. It offers a rich set of new technologies and techniques to bring solutions to various educational problems such as the courses recommendation engine, the prediction of learner behaviour, etc. This article presents the big data paradigm, its components, technologies, and characteristics. It proposes an approach for incorporating big data, online learning systems, and cloud computing in order to enhance the efficiency of the distance learning environment. Also, it provides a methodology to store and process the data produced by online learning platforms using advanced big data technologies and tools. Moreover, it explores the advantages and benefits that big data offer to students, teachers and online learning professionals.

Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.

The development of science and technology has made great progress. As an emerging technology in recent years, the research of big data (BD) technology has become more mature, and the society has also made full use of BD. The application of BD technology in the field of college education can make college education more intelligent and convenient. With the change of the times, the methods of mental health (MH) education for college students should also keep pace with the times and adopt more matching and in-depth methods, so as to provide higher quality MH education for college students and enable the development of higher education. Based on BD technology, this paper constructs an ecological MH education system for college students. It uses the form of online questionnaire to investigate and research the MH of 200 college students, and uses the constructed ecological MH education system for college students. Perform BD analysis on the collected data and draw relevant conclusions. The content of the investigation in this article is fully in line with the living conditions of college students today. This study can provide a good reference for the research on ecological MH education of college students.

This paper provides an overview of big data technologies and best practices from the standpoint of business intelligence (BI) applications in the banking industry. The authors discussed current challenges in banking industry that could be addressed by using big data technologies. Based on their research, they provided a list of big data tools and technologies in terms of an ecosystem that are suitable for real-time data processing and capable in bank fraud detection and prevention, and other bank risk analysis. They highlighted how business intelligence could be leveraged with the help of emerging big data technologies.

With the rapid changes of scientific technologies, the explosion of big data and the current COVID-19 pandemic, the internal and external environments of traditional Chinese medicine (TCM) informatics are changing rapidly and the development of TCM informatics faces numerous challenges such as digital transformation. To meet the challenges, there is a strong need to apply big data and big data analytics and technologies to TCM informatics and to incorporate data science education into the programs of TCM informatics. The purpose of this paper is to identify the key challenges facing programs of TCM informatics, and to discuss the opportunities in data science education in TCM programs. The key identified challenges are how to enable students to have a good mastery of knowledge, skills and competencies of big data analytics and technologies and how to incorporate big data analytics and technologies and the key aspects of data science education into the curricula of TCM informatics. The main opportunities are that big data and data science education can assist in meeting the challenges facing TCM informatics and offer the analytics, technologies and applications needed to improve the learning, teaching and research outcomes and standards of TCM informatics and to review, revise and constantly update the teaching contents and curriculum knowledge system of TCM informatics. This paper will be useful and helpful to the programs of TCM informatics in their curriculum design as well as the application of big data analytics and technologies to teaching, learning and research, and to faculty, students, researchers and practitioners.