Introduction Of Big Data Research Articles

Innovative Cloud Computing Techniques for Big Data Administration Getting Past Roadblocks and Developing New Paths

The digital era's introduction of big data has fundamentally altered the landscape of information management, providing organizations across a wide range of industries with unprecedented opportunities and difficulties. This scientific inquiry digs into the convergence of cloud computing and big data, presenting a thorough history of their evolution and underlining their critical roles in modern data governance. Through a meticulous examination of the critical role of effective big data management across diverse industries, the paper emphasizes the critical importance of cloud computing solutions in addressing the multifaceted complexities arising from data volume, velocity, variety, veracity, and security. Conventional big data handling approaches inside cloud computing frameworks, such as batch processing, stream processing, data warehousing, and virtualization, are thoroughly evaluated in light of their inherent constraints. The paper then examines modern cloud computing solutions that have been precisely tuned to handle the issues of scalability, real-time processing, and security inherent in large data management. The investigation is complemented by the inclusion of informative case studies and real-world examples that demonstrate the practical applicability of these pioneering ideas across a wide range of sectors, from entertainment to manufacturing. Furthermore, the paper defines emerging technologies and trends in cloud computing, highlights persistent obstacles, and outlines prospects for innovation and future research in the field. Ethical and regulatory aspects such as data privacy, fairness, openness, and compliance are extensively reviewed and emphasized. Finally, the article recommends for ongoing innovation, academic investigation, and ethical discernment to fully realize the promise of big data while limiting related hazards, emphasizing the dynamic nature of big data governance within the context of cloud computing. Key Words: Cloud computing, Big data, Hadoop ,Spark, AI and machine learning integration Blockchain

The Transformation of a Science by a “Technology”: The Case of Astrophysics and the Introduction of Machine Learning

Abstract This article explores the transformations of an area of scientific research provoked by the introduction of big data and the possibilities to treat them by Artificial Intelligence. We study these transformations in contemporary astrophysics as it has developed over the last thirty years. These transformations extend from cognitive work procedures to information processing, systems of collaboration between scientists, epistemology and terrain of questions.

Intellectual Core in Supply Chain Analytics: Bibliometric Analysis and Research Agenda

Supply chain management has evolved from local and regional purchasing and supply activities prior to the industrial revolution to the current form of technology-led, data-driven, collaborative, and global supply network. Data-driven technologies and applications in supply chain management enable supply chain planning, performance, coordination, and decision-making. Although the literature on procurement, production, logistics, distribution, and other areas within the supply chain is rich in their respective areas, systematic analyses of supply chain analytics are relatively few. Our objective is to examine supply chain analytics research to discover its intellectual core through a detailed bibliometric analysis. Specifically, we adopt citation, cocitation, co-occurrence, and centrality analysis using data obtained from the Web of Science to identify key research themes constituting the intellectual core of supply chain analytics. We find that there has been increasing attention in research circles relating to the relevance of analytics in supply chain management and implementation. We attempt to discover the themes and sub-themes in this research area. We find that the intellectual core of SCA can be classified into three main themes: (i) introduction of big data in the supply chain, (ii) adoption of analytics in different functions of operations management like logistics, pricing and location, and (iii) application of analytics for improving performance and business value. The limitations of this study and related future research directions are also presented.

The views about Big Data among professionals of police forces: A scoping review of empirical studies

There is a growing tendency toward the introduction of Big Data in police departments to improve the prevention and investigation of crime. However, there is little systematic knowledge about the perspectives of professionals in police forces regarding this technology. This article fills this gap by presenting a scoping review that systematizes empirical studies of the views of professionals in police forces about Big Data in the field of policing and criminal investigation. Fourteen articles were analyzed following a descriptive–analytical method. Optimistic and oppositional views about Big Data among professionals in police forces were then described. Optimistic views focused on the potential of Big Data to improve the objectivity and efficiency of policing and better manage police resources by providing new capabilities and strategies by which to perform crime predictions, risk assessments, criminal investigations, crime analysis, risk management, cooperation and data exchange. Oppositional views related to the police's awareness of Big Data's biases, the lack of a regulatory landscape, misuse of data, privacy threats, data security, misplaced trust in technology, the absence of major changes in work practices, and practical barriers. Analysis of the co-presence of optimistic and oppositional views adds a comprehensive and interpretative argument that might contribute to critical reflections on the technological feasibility, societal usability, and desirability of Big Data technologies in police departments.

Big Data and Related Model Algorithms in Commercial Bank Credit Evaluation

Contemporarily, with the development of big data and related machine learning models and algorithms, commercial banks no longer possess advantages in traditional risk control and need to introduce big data and its algorithmic models as a new means of risk evaluation. The introduction of big data and its model algorithms by some commercial banks and fintech companies has proven its outstanding effect in the field of risk control. Based on the evaluations of XGBoost model, OneClassSVM model and other models, the article analyses the application of related technologies in the fields of default prediction and public opinion identification. Besides, this study combines the analysis with the scenarios in which commercial banks can implement big data and model algorithms for risk evaluation. In this case, it provides a new feasible method for commercial banks' risk evaluation and control, and proves the possibility of the application of related technologies with great effect. These results play an important role in promoting the application of big data and its models for risk evaluation and control in commercial banks.

E-COMMERCE, BIG DATA, ESSENTIAL FACILITIES DOCTRINE IN COMPETITION LAW

With our entry into the era of industry 4.0 brings all activities, one of which is business to an online base commonly called e-commerce. However, with all the conveniences provided through buying and selling on online platforms, there is a new conversation about whether online platforms make competition stronger or actually facilitate market monopolies. Especially when discussing digital platforms, it's impossible to ignore market-related information, which comes in all types of information. Nonetheless, with the introduction of Big Data, previously raw data may now be processed to provide a competitive advantage to business actors who have access to it, and this facilitates the occurrence of exploitation if the business actor does not choose to share access to Big Data. Hence, a regulation is needed to discuss the ownership of Big Data access in an online platform and how the role of the essential facilities doctrine as a basis so that business actors do not hold back is actually required to provide access to other competitors in order to access these essential facilities.

Exploring City Image Perception in Social Media Big Data through Deep Learning: A Case Study of Zhongshan City

Based on Kevin Lynch’s cognitive method of urban image and Weibo’s review data, this study constructs a research framework with three modules as the core: city image structure, city image types, and cultural service evaluation. First, the geospatial information carried by comments is analyzed by GIS to obtain the image structure of the city; second, the picture data in the comments are divided into image types and the type ratio is calculated by the image semantic segmentation method based on deep full convolution neural network. Finally, the text data in the comments are extracted from the semantic word frequency analysis to evaluate the cultural service perception index words of the city image and combined with the analysis of the city image structure and the city image type so as to obtain the integrated comprehensive perception of the city image. The research shows that the introduction of big data and deep learning methods into city image research can make up for the shortcomings of traditional research samples, expand the dimension and breadth of urban cognition, reveal the social, cultural, and functional characteristics of the city, and is an important supplement to the five-element model of city image depicting the material form of the city. In addition, the results of the empirical study, taking Zhongshan City as an example, have implications for the realistic urban spatial planning, urban landscape design, and tourism industry layout of Zhongshan.

The Use of Big Data in the Take-out and Delivery Market in China

The initial development of the takeout industry was in the form of packaging. Later, with the popularity of telephones and mobile phones, people began to order by telephone to meet the needs of some customers who didn’t want or didn’t have time to go to restaurants. Since the 1990s, the Internet began to popularize, which made the takeout industry develop unprecedentedly. Now, after several years of rapid development, the takeout industry has already developed a mature industrial chain. Although it is a labor-intensive industry, the current takeout industry has joined Big Data, cloud computing, Internet of things and other technologies. Especially in recent years, with the introduction of Big Data, major takeout companies have developed their own Big Data analysis system to continuously improve and optimize the takeout system through the introduction and analysis of a large amount of data. This paper aims to study the boosting effect of Big Data on the takeout industry, especially how to analyze and optimize the takeout time, takeout path and so on through Big Data analysis in China’s takeout market.

Третья платформа информации и Big Data

Despite the fact that the introduction of Big Data in many areas of life is an inevitable reality of the near future, there are difficulties with the classification of information, the allocation of knowledge, storage, security and scalability issues. The paper describes the concept of the third platform of informatization and Big Data, which play an important role in this process, is described. The problems associated with the need for efficient processing and analysis of Big Data are posed. The spheres of life in which new technologies are being actively introduced and developed are shown and considered. The purpose of the study is to discuss the trends in the implementation of Big Data within the framework of the third informatization platform. The problems of Big Data and some ways to solve them are investigated. The interrelations and patterns in the field of Big Data and their impact on the development of information technologies are considered. Research methodology: analytical research methods based on the analysis of data and concepts of the third informatization platform are used. The results of the study: a deep analysis was carried out for full immersion in the issue of the third platform of informatization and the concept of Big Data. The advantages of introducing technology components into the spheres of life are shown, as well as the problems of Big Data and some ways of their solution are studied. The main theoretical results of the study are presented, confirming the interrelationships and patterns between the elements of developing information technologies. The practical significance of the third informatization platform and its components is discussed. Recommendations related to the application of these technologies in real conditions are proposed, as well as their mutual influence of technologies to achieve more effective results.

The Concept of Big Data and Solutions of Cloud Computing

The terms "big data" and "cloud computing" are often used interchangeably since many public cloud services analyze large amounts of data. Every day, a massive amount of data is produced from abundant of derivation, and this data needs to be evaluated, categorized, and stored with the assistance of cloud computing. The quick progress that has taken place in a variety of IT fields is to blame for this massive increase in the amount of data that is being produced. Conventional data processing methods are unable to handle this data due to its extremely large amount, the rapidity with which it changes, and the multitude of formats in which it is stored. Cloud computing makes available on-demand computing resources and services, making it possible to handle and analyze the vast amounts of data in a manner that is both effective and simplified. This research will discuss how on the general concept of big data and how Cloud computing provides solutions to big data by processing analyzing this huge data. We will start by Introduction of Big data, big data Impact on Today’s world, big data architecture, cloud computing, Categories & offerings of Cloud computing then further discuss the solutions offered by cloud computing to big data and limitations of big data on the cloud.

The Optimization Strategy of Innovation and Entrepreneurship Education in Colleges and Universities under the Background of Big Data

The introduction of big data into innovation and entrepreneurship education in Colleges and universities is of positive significance in achieving the accuracy of information, personalization of education methods and scientization of education platforms. To improve the quality of innovation and entrepreneurship education in Colleges and universities, the innovation of educational ideas and means need to be strengthened with the support of big data. This paper mainly expounds the significance and countermeasures of improving the quality of college students' innovation and entrepreneurship education from the perspective of big data, hoping to play a positive reference role in the reform of innovation and entrepreneurship education, make up for the shortcomings of the traditional education model, and promote the realization of the training goal of innovation and entrepreneurship talents in Colleges and universities as soon as possible.

The Adaptation of Internet of Things in the Indian Insurance Industry—Reviewing the Challenges and Potential Solutions

The concept of insurance was found several centuries before Christ. Correspondingly, Chinese and Babylonian traders practiced moving or dispensing risks in the second and third millennia BC. Nowadays, insurance is the backbone of the economy. The recent introduction of big data, IoT, and other forms of InsurTech led to the fourth industrial revolution in insurance in the developed world. The industry is looking to improve the ergonomics of remote sensing technology to improve the acceptability of the clients. The adaptation of IoT in developing economies may provide a solution in increasing insurance penetration. This study explores the challenges and solutions in adopting IoT to increase insurance penetration in India. This study applied a systematic literature review (SLR) to extract the themes/variables related to challenges and solutions in adopting IoT in India’s insurance sector. Several keywords were used to search the relevant literature from Google Scholar. Based on inclusion and exclusion criteria, the filtered studies were explored. This study listed several challenges and their solutions in the adaption of IoT in the Indian insurance industry. Policymakers could adapt the suggestions provided to improve the service delivery insurance sector. The authors listed several challenges and solutions in the adaption of IoT in the Indian insurance industry through a systematic literature review to facilitate the policymakers to make the right decisions.

Predictive Maintenance in the Automotive Sector: A Literature Review

With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance.

DATAFICATION AS A NECESSARY STEP IN THE PROCESSING OF BIG DATA IN DECISION-MAKING TASKS OF BUSINESS

The digital transformation of business in the light of opportunities and focusing on the challenges posed by the introduction of Big Data in enterprises allows for a more accurate reflection of the internal and external environmental stimuli. Intuition ceases to be present in the decision-making process, and decision-making becomes strictly data-based. Thus, the precondition for data-based decision-making is relevant data in digital form, resulting from data processing. Datafication is the process by which subjects, objects and procedures are transformed into digital data. Only after data collection can other natural steps occur to acquire knowledge to improve the company's results if we move in the industry's functioning context. The task of finding a set of attributes (selecting attributes from a set of available attributes) so that a suitable alternative can be determined in its decision-making is analogous to the task of classification. Decision trees are suitable for solving such a task. We verified the proposed method in the case of logistics tasks. The analysis subject was tasks from logistics and 80 well-described quantitative methods used in logistics to solve them. The result of the analysis is a matrix (table), in which the rows contain the values of individual attributes defining a specific logistic task. The columns contain the values of the given attribute for different tasks. We used Incremental Wrapper Subset Selection IWSS package Weka 3.8.4 to select attributes. The resulting classification model is suitable for use in DSS. The analysis of logistics tasks and the subsequent design of a classification model made it possible to reveal the contours of the relationship between the characteristics of a logistics problem explicitly expressed through a set of attributes and the classes of methods used to solve them.

Design optimisation of a wireless sensor node using a temperature-based test plan

<p class="Abstract"><span lang="EN-US">The introduction of Big Data and Internet of Things has allowed the rapid development of smart farming technologies. Usually, systems implemented in smart farms monitor environmental conditions and soil parameter to improve productivity, to optimize soil conservation, to save water and to limit plant diseases. Wireless sensor networks are a widespread solution because they allow to implement effective and efficient crop monitoring. At the same time, wireless sensor networks can cover large area, they can ensure fault tolerance and they can acquire large amount of data. Recent literature misses to consider the testing of the hardware performances of such systems according to the actual operating conditions. The effects of a harsh environment on the dynamic metrological performances of sensor nodes are not sufficiently investigated. Consequently, this work deals with the electrical design optimization of a sensor node by means of thermal test used to reproduce the actual operating conditions of the nodes. The results of the node characterization through thermal tests are used to improve the node’s design and consequently to achieve higher performances in harsh operative conditions.</span></p>

Privacy Protection in Bigdata: A Survey

Bigdata becomes a significant sector and academics research topic. Bigdata is a two-edged sword. The rising volume of information together will increase the likelihood of blundering non-public data privacy. Due to many new technologies and innovations that pervade our everyday lives, like smartphones and social networking apps, and the Internet of Things-based intelligent-world systems, the large amount of data generated in our world has exploded. During this data processing, storage, and the use of the information it can quickly cause personal information exposure and the difficulty of interpreting the information. The aim is to incorporate this range of information into one framework for big data management and to recognize problems regarding privacy. This paper begins with the introduction of bigdata, its process, protection issues, and tools which are used to solve its problems

A Review of Traffic Congestion Prediction Using Artificial Intelligence

In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters. Most of the researches focus on historical data in forecasting traffic congestion. However, a few articles made real-time traffic congestion prediction. This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models. The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised.

User-centric genomics infrastructure: trends and technologies.

Genomics is both a data- and compute-intensive discipline. The success of genomics depends on an adequate informatics infrastructure that can address growing data demands and enable a diverse range of resource-intensive computational activities. Designing a suitable infrastructure is a challenging task, and its success largely depends on its adoption by users. In this article, we take a user-centric view of the genomics, where users are bioinformaticians, computational biologists, and data scientists. We try to take their point of view on how traditional computational activities for genomics are expanding due to data growth, as well as the introduction of big data and cloud technologies. The changing landscape of computational activities and new user requirements will influence the design of future genomics infrastructures.

A Contemporary Study of Deep Learning in Diagnosis and Prediction of Diseases

With the introduction of big data and its incredible advancement in image procurement devices, the transformation of medical data into valuable knowledge has become an important challenge in the area of bioinformatics. The medical images procured require huge analysis and diagnosis of images which can be done using Artificial Intelligence techniques like Machine learning and Deep Learning that yields automated diagnosis solutions. Deep learning methods can provide optimized and precise solutions for medical image diagnosis and can be an important methodology for imminent health care applications. In this paper, some contemporary deep learning neural networks will be discussed and their application in detecting various diseases.

Is Big Digital Data Different? Towards a New Archaeological Paradigm

ABSTRACTArchaeological data is always incomplete, frequently unreliable, often replete with unknown unknowns, but we nevertheless make the best of what we have and use it to build our theories and extrapolations about past events. Is there any reason to think that digital data alter this already complicated relationship with archaeological data? How does the shift to an infinitely more flexible, fluid digital medium change the character of our data and our use of it? The introduction of Big Data is frequently said to herald a new epistemological paradigm, but what are the implications of this for archaeology? As we are increasingly subject to algorithmic agency, how can we best manage this new data regime? This paper seeks to unpick the nature of digital data and its use within a Big Data environment as a prerequisite to rational and appropriate digital data analysis in archaeology, and proposes a means towards developing a more reflexive, contextual approach to Big Data.