Big Data Environment Research Articles

Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy

More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment.

A Privacy Risk Assessment Model for Medical Big Data Based on Adaptive Neuro-Fuzzy Theory

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.

Research on Systematic Risk Early Warning System Based on Machine Learning Technology: A Case Study of Marine Economy

Zhao, J.; Yu, J., and Wang, X., 2020. Research on systematic risk early warning system based on machine learning technology: A case study of marine economy. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 230–233. Coconut Creek (Florida), ISSN 0749-0208.With the rapid development of information technology, the value contained in big data has attracted more and more attention. Conducting efficient analysis of big data has become an important topic. Machine learning is one of the commonly used methods for data analysis. The traditional machine learning algorithm is often designed as the way of offline batch training. However, this method is not suitable for data sets with massive scale and continuous growth in the big data environment. Transforming the traditional machine learning algorithm so that it can better apply to the big data environment has become a research hotspot. With the rapid development of economy and society and the continuous progress of science and technology, the public's understanding of ocean functions is gradually deepened, the demand for marine products and services is increasing every day, and the economic and social benefits of the ocean are continuously rising. In this context, great importance should be given to the examination of marine resources and environment, the development and utilization of work, the constant adjusting of the ocean economic development policy, and the variety of comprehensive marine management measures to ensure the sustainable development of implementation of marine programs. The current mainstream of machine learning technology based on the marine economy of systemic risk early warning system for research is proposed here.

Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data

Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research on face recognition, small-scale face recognition has achieved good recognition results, but in the era of big data, the existing small-scale face recognition methods have gradually failed to meet the social needs, and how to get a good face recognition effect in the era of big data has become a new research hotspot. Based on this, this paper aims to optimize the existing face recognition algorithm, study the face recognition method driven by big data, and propose a deep learning multi feature fusion face recognition algorithm driven by big data. First, for the problem that 2DPCA (Two-dimensional Principle Component Analysis) can well extract the global features of the face under large samples, but the local features of the face are difficult to process, this paper uses the LBP (Local Binary Pattern, LBP) algorithm to extract the texture features of the face, and the extracted texture features are integrated with the global features extracted by 2DPCA to multi-feature fusion, so that the fused features can take into account both global and local features, and have better recognition results. Then using the obtained fusion features as input, training in a convolutional neural network, and measuring the similarity based on the feature vectors of the sample set and the training set after the training, can realize multi-feature fusion face recognition. Through the analysis of simulation experiments, it is found that, compared with the use of global features or local features alone, the fusion features obtained by multi-feature fusion of global features extracted by 2DPCA and local features extracted by LBP algorithm have better recognition effect in the big data environment. After convolutional neural network trains and recognizes this feature, a high recognition accuracy rate is obtained, which can show that the face recognition method designed in this paper has good application potential in the era of big data. In the background of big data, the accuracy of face recognition can reach more than 90%, which can meet the needs of society well.

Research on Credit Risk Measurement of Small and Micro Enterprises Based on the Integrated Algorithm of Improved GSO and ELM

Small and micro enterprises play a very important role in economic growth, technological innovation, employment and social stability etc. Due to the lack of credible financial statements and reliable business records of small and micro enterprises, they are facing financing difficulties, which has become an important factor hindering the development of small and micro enterprises. Therefore, a credit risk measurement model based on the integrated algorithm of improved GSO (Glowworm Swarm Optimization) and ELM (Extreme Learning Machine) is proposed in this paper. First of all, according to the growth and development characteristics of small and micro enterprises in the big data environment, the formation mechanism of credit risk of small and micro enterprises is analyzed from the perspective of granularity scaling, cross-border association and global view driven by big data, and the index system of credit comprehensive measurement is established by summarizing and analyzing the factors that affect the credit evaluation index. Secondly, a new algorithm based on the parallel integration of the good point set adaptive glowworm swarm optimization algorithm and the Extreme learning machine is built. Finally, the integrated algorithm based on improved GSO and ELM is applied to the credit risk measurement modeling of small and micro enterprises, and some sample data of small and micro enterprises in China are collected, and simulation experiments are carried out with the help of MATLAB software tools. The experimental results show that the model is effective, feasible, and accurate. The research results of this paper provide a reference for solving the credit risk measurement problem of small and micro enterprises and also lay a solid foundation for the theoretical research of credit risk management.

Online corporate reputation: A panel data approach and a reputation index proposal applied to the banking sector

Managing an online corporate reputation (OCR) is increasingly strategic to improving the corporation’s economic performance. What becomes crucial is to collect, process and analyse the comments expressed in the online medium, considering them as an intangible asset with an impact on tangible assets and consequences on business value. This research contributes to show the potential of the big-data environment, enhanced by the use of business intelligence tools and combined with panel econometric models. This is complemented by the elaboration of an Online Corporate Reputation Index (OCRI) to measure the economic corporate performance. The findings show that after applying the conceptual model to the banking sector, the experience associated with ethics is the intangible asset that has the most impact on the economic performance associated with each bank. In addition, the definition of an index helps to position one company in relation to another and to an entire economic sector.

A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data

One of the most common tasks nowadays in big data environments is the need to classify large amounts of data. There are numerous classification models designed to perform best in different environments and datasets, each with its advantages and disadvantages. However, when dealing with big data, their performance is significantly degraded because they are not designed—or even capable—of handling very large datasets. The current approach is based on a novel proposal of exploiting the dynamics of skyline queries to efficiently identify the decision boundary and classify big data. A comparison against the popular k-nearest neighbor (k-NN), support vector machines (SVM) and naïve Bayes classification algorithms shows that the proposed method is faster than the k-NN and the SVM. The novelty of this method is based on the fact that only a small number of computations are needed in order to make a prediction, while its full potential is revealed in very large datasets.

An efficient ACO-PSO-based framework for data classification and preprocessing in big data

Big data is prominent for the systematic extraction and analysis of a huge or complex dataset. It is also helpful in the management of data as compared to the traditional data-processing mechanisms. In this paper, an efficient ant colony optimization (ACO) and particle swarm optimization (PSO)-based framework have been proposed for data classification and preprocessing in the big data environment. It shows that the content part can be collaborated and fetched for analysis from the volume and velocity integration. Then weight marking has been done through the volume and the data variety. In the end, the ranking has been done through the velocity and variety aspects of big data. Data preprocessing has been performed from weights assigned on the basis of size, content, and keywords. ACO and PSO are then applied considering different computation aspects like uniform distribution, random initialization, epochs, iterations, and time constraint in case of both minimization and maximization. The weight assignments have been done automatically and through an unbiased random mechanism. It has been done on a scale of 0–1 for all the separated data. Then simple adaptive weight (SAW) method has been applied for prioritization and ranking. The overall average classification accuracy obtained in the case of PSO-SAW is 98%, and in the case of ACO-SAW, it is 95%. PSO-SAW approach outperforms in all cases, in comparison to ACO-SAW.

A Intelligent Evaluation Model Framework of Online Public Opinion Crisis in Higher Vocational Colleges

Higher vocational colleges should establish a real-time online public opinion monitoring mechanism. Before deterioration trend occurs, take relevant measures to relieve and guide. The current ability to deal with online public opinion crisis needs to improve. This paper puts forward the principle of the warning index system of the online public in higher vocational colleges, designs a new evaluation index system, builds the intelligent evaluation model framework based on AHP, fuzzy evaluation method and BP neural network which can effectively learn the knowledge and experience of evaluation experts. This model framework can make full use of available data in the big data environment and reduce manpower.

Research on innovation mode of new material enterprises under the background of big data

In the background of big data, the innovation process of new material enterprises is the result of full and effective integration of many external forces. This paper analyzes the impact and challenges of the emerging big data phenomenon on new material enterprise innovation, including internet plus, material genome project and so on. This paper also puts forward the mechanism of external driving force for innovation of new material enterprises in the big data environment, and discusses the influence of DMAIC model on the decision-making of new material enterprises. Finally, the paper expounds how to establish a multivariate and opening innovation platform for new material enterprises based on big data ecosystem under the two major support systems of internet of things and cloud computing, so as to make full use of various big data resources to serve the innovation and development of new material enterprises.

Online Joint Placement and Allocation of Virtual Network Functions With Heterogeneous Servers

Network function virtualization (NFV) is a promising virtualization technology that has the potential to significantly reduce the expenses and improve service agility. The NFV makes it possible for Internet service providers (ISPs) to employ various virtual network functions (VNFs) without installing new equipments. One of the most attractive approaches in the NFV technology is the so-called joint placement and allocation of virtual network functions (JPA-VNFs), which considers the balance between VNF investment with Quality of Services (QoS). We introduce a novel capability function to measure the potential of locating VNF instances for each server in the proposed OJPA-HS model. This model allows the servers in the network to be heterogeneous, at the same time combines and generalizes many classical JPA-VNF models. Despite its NP-hardness, we present a provable best-possible deterministic online algorithm based on dynamic programming (DP). To conquer the high complexity of DP, we propose two additional randomized heuristics, Las Vegas (LV) and Monte Carlo (MC) randomized algorithms, which perform even as good as DP with much smaller complexity. Besides, MC is a promising heuristic in practice as it has the advantage to deal with the big data environment. Extensive numerical experiments are constructed for the proposed algorithms in this article.

Data Analysis and Application Research in Sports Field under Big Data Environment

This paper expounds data mining theory and sports data analysis theory, such as data mining concept, mining method and mining flow. Taking the data analysis in basketball and track and field as an example, this paper discusses the application of data mining in the field of sports industry, mass physique monitoring and physical education data analysis at home and abroad, and analyzes the influence of big data on sports, mainly changing the methods of competitive sports training, promoting the innovation of sports research methods and the transformation of sports communication methods, and promoting the development of the world sports industry.

Secured smart mobile app for smart home environment

Smart home appliances like smart door, smart AC, smart lights etc., are the facilities which can be utilized because of advancements in Internet of Things. However, security of these smart home appliances is very much important to avoid security threats. Existing smart home appliances are facing problems with security issues such as such as hacking, mishandling of devices etc., due to one stage authentication process. The proposed Secured Smart Mobile App for Smart Home Environment (SSMASHE) provides a two stage authentication process which can secure smart home appliances. It consists of three modules, first one is smart things, next one smart mobile app and the third module is big data platform. In this paper a smart home environment embedding with smart mobile app has been simulated to evaluate the security mechanism through authentication process. In this process the delay has been calculated for the purpose of fast communication and security assigned to big data environment. The proposed method delay time is less when compared to Fast Identity Online (FIDO) method. The proposed method is comfortable to real time heterogeneous smart home IoT devices because this process follows the asynchronous authentication procedure.

Privacy-Preserving Lightweight Data Monitoring in Internet of Things Environments

The fast development of Internet of Things (IoT) has shown that it becomes one of the most popular techniques. In the IoT paradigm, ubiquitous sensors and smart devices can be interconnected to collect various status data and share with others. When deployed in an environment status monitoring system, distributed sensors may be requested to periodically report real-time data. The large-scale data would make the system controller unable to process in time. In this case, a third-party server can be engaged to conduct most of monitoring work, where sensors direct report to the server to generate intermediate monitoring results for the system controller. However, the server may be curious about the contents of outsourced system standing queries, data vectors of sensors, and monitoring results. In addition, due to the limited computing resources of distributed sensors, existing cryptographic solutions are not applicable to such monitoring scenario. To address these issues, this paper proposes a lightweight server-aided data monitoring scheme (SIM). Thorough efficiency and privacy analysis confirm the practicality of the proposed SIM scheme. Moreover, this paper extends Lu et al.’s privacy-preserving cosine similarity computing protocol in the two-party setting in big data environment to support computing on any dimensional data, without incurring expensive calculations.

A survey and analysis of big data management based on computational methodologies

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

Load balancing strategy for medical big data based on low delay cloud network

Since the cloud servers are far away from the medical detection terminal and user terminal, the communication overhead such as the delay caused by data transmission is large. At the same time, a large number of medical terminals and user terminals access the cloud servers, which makes the cloud servers overloaded, the overall robustness of the network is poor, and the network is prone to failure, which may lead to the work efficiency of doctors cannot be guaranteed, and the waiting time of patients will also increase. To solve the above problems, according to the characteristics of dynamic resource allocation in the medical big data environment, a new cloud network architecture is proposed. To solve the resource scheduling problem, a chaotic algorithm is introduced into the artificial firefly algorithm, and a load balancing optimisation strategy based on a chaotic firefly algorithm is proposed. The simulation results show that the convergence rate of the proposed algorithm is accelerated by adding chaos factor, to avoid the algorithm falling into the local optimal solution. Compared with other load balancing algorithms, the proposed algorithm is more suitable for solving the resource scheduling problem of large-scale tasks in cloud-fog networks.

Secure Data Duplication Checking with Backup Recovery in Big Data Environments

With the rapidly increasing amounts of data produced worldwide, networked and multi- user storage systems are becoming very popular. However, concerns over data security still prevent many users from migrating data to remote storage. The conventional solution is to encrypt the data before it leaves the owner’s premises. While sound from a security perspective,this approach prevents the storage provider from effectively applying storage efficiency functions, such as compression and deduplication, which would allow optimal usage of the resources and consequently lower service cost. Client-side data deduplication in particular ensures that multiple uploads of the same content only consume network bandwidth and storage space of a single upload. Deduplication is actively used by a number of backup providers as well as various data services. In this project, we present a scheme that permits the storage without duplication of multiple types of files. And also need the intuition is that outsourced data may require different levels of protection. Based on this idea, we design an encryption scheme that guarantees semantic security for unpopular data and provides weaker security and better storage and bandwidth benefits for popular data. This way, data deduplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. We can use the backup recover system at the time of blocking and also analyze frequent log in access system.

Survey on Security Monitoring and Intrusion Detection in the Big Data Environment

Survey on Security Monitoring and Intrusion Detection in the Big Data Environment

Investment decision and coordination of green agri-food supply chain considering information service based on blockchain and big data

Researches about the fusion application of Big Data and blockchain have appeared for a long time, many information service providers have launched information service business based on Big Data and blockchain (hereafter, ISBD). However, in the green agri-food area, the ISBD application does not popularized. A vital reason is that many decision makers do not know how to make an optimal investment decision and coordinate chain members after adopting ISBD. The core of this problem is to study the issue of investment decision and coordination in a green agri-food supply chain. To solve this problem, firstly, combining with the status of Chinese agricultural development, we proposed a more suitable supply chain structure in the fusion application environment of Big Data and blockchain. Then, we chose a green agri-food supply chain with one producer and one retailer as research object and revised the demand function. Afterwards, considering the changes of agri-food freshness and greenness, we built and analysed the benefit models of producer and retailer before and after using ISBD, and then a cost-sharing and revenue-sharing contract was put forward to coordinate the supply chain. Findings: 1) When the total investment cost payed by producer and retailer is in a certain range, using ISBD will help chain members gain more benefits. 2) If chain members want to gain more benefits after using ISBD, they should try their best to optimize costs by extracting valuable information. Results can offer a theoretical guidance for producer and retailer in investing in ISBD, pricing decision and supply chain coordination after applying ISBD.

Research and application of space-time behavior maps: a review

ABSTRACT Today, in the space-time big data environment, the research into behavior maps, as an important spatial analysis and behavior visualization method, has gradually increased in dimension, depth and breadth. In this study, we used the China National Knowledge Infrastructure (CNKI) database and the Web of Science (WOS) Core Collection database as the main literature search engines, through the literature review, the behavior maps analysis type, visual expression, and its method evolution are summarized. And we used CiteSpace and VOSviewer scientific knowledge mapping software to reveal the hotspots, development trends, and field dynamics of space-time behavior maps research in the collected documents. We find that the current research literature on space-time behavior maps shows three areas of clustering: post-occupancy evaluation, public open space, and time geography. By establishing the typical application cases in the three fields, the applications and development frontiers are summarized and considered.