Role Of Big Data Research Articles

An Indispensable role of Big Data in Healthcare

An Indispensable role of Big Data in Healthcare

Role of Big Data in Enhancing HVAC Operational Efficiency

Role of Big Data in Enhancing HVAC Operational Efficiency

The Role of Big Data in Industry 4.0 in Mining Industry in Serbia

Abstract The current age characterized by unstoppable progress and rapid development of new technologies and methods such as the Internet of Things, machine learning and artificial intelligence, brings new requirements for enterprise information systems. Information systems ought to be a consistent set of elements that provide a basis for information that could be used in context to obtain knowledge. To generate valid knowledge, information must be based on objective and actual data. Furthermore, due to Industry 4.0 trends such as digitalization and online process monitoring, the amount of data produced is constantly increasing – in this context the term Big Data is used. The aim of this article is to point out the role of Big Data within Industry 4.0. Nevertheless, Big Data could be used in a much wider range of business areas, not just in industry. The term Big Data encompasses issues related to the exponentially growing volume of produced data, their variety and velocity of their origin. These characteristics of Big Data are also associated with possible processing problems. The article also focuses on the issue of ensuring and monitoring the quality of data. Reliable information cannot be inferred from poor quality data and the knowledge gained from such information is inaccurate. The expected results do not appear in such a case and the ultimate consequence may be a loss of confidence in the information system used. On the contrary, it could be assumed that the acquisition, storage and use of Big Data in the future will become a key factor to maintaining competitiveness, business growth and further innovations. Thus, the organizations that will systematically use Big Data in their decision-making process and planning strategies will have a competitive advantage.

Legal Challenges in the Realm of InsurTech

The paper addresses the topic of digital innovation in the insurance field and the challenges it poses for both companies and regulators. The paper starts with showing how data-driven technologies represent a priceless resource for insurers, allowing new and revolutionary methods to classify policyholders in different merit categories according to risk. The paper then turns to the analysis of new tools at the centre of the recent technological development (i.e. Blockchain and smart contracts) and focuses on the role of Big Data in the insurance sector. The paper thus concludes with some considerations on why access to more types of personal data should be allowed to insurers by regulators, presenting potential advantages not only for the companies themselves, but also for customers and the financial system in general. FinTech, InsurTech, Innovation, BigData, Blockchain, Technology, Personal Data, Insurance, Credit Scoring, Risk.

Key Topics and Future Perspectives in Natural Hazards Research

Since early 2018 the “Natural Hazards” Section of Geosciences journal has aimed to publish pure, experimental, or applied research that is focused on advancing methodologies, technologies, expertise, and capabilities to detect, characterize, monitor, and model natural hazards and assess their associated risks. This stream of geoscientific research has reached a high degree of specialization and represents a multi-disciplinary research realm. To inaugurate this section, the Special Issue “Key Topics and Future Perspectives in Natural Hazards Research” was launched. After a year and half since the call for papers was initially opened, the special issue is now completed with the editorial introducing the collection of 10 selected papers covering the following hot topics of natural hazards research: (i) trends in publications and research directions at international level; (ii) the role of Big Data in natural disaster management; (iii) assessment of seismic risk through the understanding and quantification of its three components (i.e., hazard, vulnerability and exposure/impact); (iv) climatic/hydro-meteorological hazards (i.e., drought, hurricanes); and (v) scientific analysis of past incidents and disaster forensics (i.e., the Oroville Dam 2017 spillway incident). The present editorial provides a summary of each paper of the collection within the current context of scientific research on natural hazards, pointing out the salient results and key messages.

Improving business process by predicting customer needs based on seasonal analysis: the role of big data in e-commerce

Many e-commerce sites give item recommendations to buyers while they navigate the site. This study aims to identify the ways to predict the customer demands based on different seasonal in India and improving the business process of a new e-commerce seller by giving recommendations. We focus on textile and we categorised the seasonal in to three winter season, summer season and rainy season. In this study we analyse historical sale record of a new e-commerce seller Esteavo International based on these three seasonal. Using these analyses, we aim to determine the purchase patterns of the customers and the factors affecting the changes in sale on different seasons. Also, we developed new big data architecture it guides the e-commerce seller for taking effective decisions to improve their business process.

Role of Big Data Analytics in Power System Application

Power system sector is the back bone for any country economic growth. In current years, electric power systems have experienced various challenges and technological innovations and have become digitalized with the introduction concept of smart grids. Power systems are being operated in a stressed condition mainly due to the ever increasing load demand, depleting energy resources and environmental constraints on Transmission line expansion. This article focus mainly role of Big Data in various industrialization in brief and specifically applied in the power system studies along with other sectors. Also focuses on using very large data collections, which are difficult to access in standard database systems and also refers to as big data, to manage and monitor the power system. System stability is an significant goal for power engineers to use this huge amount of data to run the system in their rated capacity, power sector can beneficial of various potential solicitation of power system by the use of large-scale data analysis that can help improve the optimization process and helps for the power system to operate in the effective manner.

The Place of Big Data in Addressing Emerged Issues in Vaccinology of the 21st Century

Vaccinology, as a great achievement of public health of the 20th century nowadays faces doubts, questions, and concerns that could be included in the term of vaccine hesitancy. The vaccinology in the 21st century is marked by the emerging anti-vaccine movements followed by a variety of attempts and approaches of professionals to resolve them. The globalization in health care on the one side and great technological achievements, on the other, create the possibilities where an enormous amount of data is publicly available. The professionals have based the benefits of vaccination on scientific data. Vaccine hesitancy and anti-vaccine movements declare that they have also based their policy on scientific data. On the first line of facing parental vaccine hesitancy are primary care pediatricians. They can testify their own limiting abilities to do so, as well as the limiting abilities of other professionals involved in vaccinology. In the situation of an enormous amount of data, they could be drowned out and interpreted in various ways. It is clear that the old-fashioned “defense” of the great public achievement of the 20 century - vaccinology - is no longer appropriate. On the other hand, a search of the literature shows the entry of “Big Data” into medicine in general and the public health and vaccinology. This paper attempts to position the role of Big Data (its benefits, traps, and ethical implications) in vaccinology in the 21st century based on the literature research and our propositions.

The role of Big Data in Astronomy Education

AbstractWe review Big Data in Astronomy and its role in Astronomy Education. At present all-sky and large-area astronomical surveys and their catalogued data span over the whole range of electromagnetic spectrum, from gamma-ray to radio, as well as most important surveys giving optical images, proper motions, variability and spectroscopic data. Most important astronomical databases and archives are presented as well. They are powerful sources for many-sided efficient research using the Virtual Observatory (VO) environment. It is shown that using and analysis of Big Data accumulated in astronomy lead to many new discoveries. Using these data gives a significant advantage for Astronomy Education due to its attractiveness and due to big interest of young generation to computer science and technologies. The Computer Science itself benefits from data coming from the Universe and a new interdisciplinary science Astroinformatics has been created to manage these data.

State-of-the-art Prototypes and Future Propensity Stem on Internet of Things

We all are living in the era of the internet that too up to the next level where each and everything has been connected to the internet. IoT has become much more popular these days with the advancement of technology and connecting physical world with the virtual world making the life of individual’s and organization easier. Recently IoT becomes more popular in the research area where many research articles have been published and reviewed. Its capacity to get associated with everything whether it's human, object, innovation, things, and so forth drawn numerous chances of advancement and extension in future. The way the technology has been evolved recently in every field we see rapid changes in the products, services, things that we are using in our daily life. IoT is platform that has becoming popular due to its simplified technology which benefits the individual’s as well as organization and end-users. Today’s IoT has given many opportunities to make easier life, effective communications and stay connected to our close and society by enabling many technologies but there are some issues which are the major concern of IoT and these issues must be resolved so that IoT can be used as revolutionary technology in future. This paper focuses on state-of–the-art porotypes on IoT, basic prerequisite in the IoT, role of big data in IoT. Enabling technologies, technology used in various applications such as smart city, agriculture, health-care, lifesaving, smart parking, urban planning. We also explore the applications of IoT and its future propensity, its architecture and comparison of IoT with H2M and M2M.

Big data in yeast systems biology.

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Role of Big Data in Chemistry Education Research

Role of Big Data in Chemistry Education Research

Implementation of Clustering Algorithms for Real Time Large Datasets

Now a day’s clustering plays vital role in big data. It is very difficult to analyze and cluster large volume of data. Clustering is a procedure for grouping similar data objects of a data set. We make sure that inside the cluster high intra cluster similarity and outside the cluster high inter similarity. Clustering used in statistical analysis, geographical maps, biology cell analysis and in google maps. The various approaches for clustering grid clustering, density based clustering, hierarchical methods, partitioning approaches. In this survey paper we focused on all these algorithms for large datasets like big data and make a report on comparison among them. The main metric is time complexity to differentiate all algorithms.

NGS Short Read Alignment Algorithms and the role of Big Data and Cloud Computing

Next Generation Sequencing (NGS) raises opportunities to the computational field for fast and accurate methods for the various challenges associated with NGS data. NGS technology generates a large set of short reads of size 50 to 400 base pairs as a

Big Data in Cardiovascular Disease

Cardiovascular diseases exert a wide-reaching epidemiological impact as the number one cause of death worldwide. Emerging technologies such as big data and artificial intelligence (AI) are poised to significantly change the field of cardiology. However, their applications are still emerging. We aimed to define the role of big data and AI in cardiovascular disease with a focus on research. There are zettabyte levels (1021 bytes) of big data in the US that can be directed towards healthcare research. There are applications of big data analytics already being put to use with genomics, heart failure readmissions, echocardiography, and many other areas within cardiology. We profile in this paper an extensive listing of various datasets used throughout the globe to study big data. Within cardiology, there is tremendous potential for the application of big data analytics in personalized patient care; however, they still require validation.

The Role of Big Data and Digitization in Just-In-Time (JIT) Information Feeding and Marketing

Marketing management is racing to keep up with the technological advances that are disrupting how consumers connect and interact with business’ brands. Marketing today needs to design and run an explosion of continuous marketing touchpoints that evolve rapidly. Speed, adaptability, and capability in marketing to balance innovation are crucial to business. Scalability in highly dynamic digital environment is also a key factor in businesses’ competitiveness. Now, modern marketing uses many contemporary software developments. In this paper on the Marketing Science Institute (MSI) Research Priority Two, delivering integrated, real-time, relevant experiences in context, Just-In-Time (JIT) information feeding via agile and lean digital marketing are discussed. How companies continuously innovate using big data and big testing are discussed as future implications for marketing practitioners and researchers. Keywords: agile marketing, big data, innovation, Just-In-Time (JIT), lean marketing

Role of Big Data in Cardiovascular Research.

Role of Big Data in Cardiovascular Research.

Limitless personalisation: the role of Big Data in unveiling service opportunities

ABSTRACTThe relevance of services is increasing in the world economy, and digital technologies are boosting this process. At the same time, customers are becoming used to receiving services that are closer to their needs and expectations, and Big Data seems to play a key role in the evolution of services. This research aims to link these two trends by studying how Big Data is re-shaping service classification, thereby impacting the variables that have been used over the years to describe and classify services. This paper studies this emerging kind of services through 12 illustrative case studies based on theoretical sampling, under two variables: the extent of customer contact and the degree of customer participation and involvement. The research offers a classification of these services, discussing them under the lenses of previous models in the service field.

Harnessing Big Data for Sustainable Development in Nigeria

Nigeria faces a myriad of development challenges in her efforts to grow the economy, create jobs and achieve the Sustainable Development Goals by 2030. Since independence, the Government has developed many Plans and Strategies, including the current Economic Recovery and Growth Plan, in an attempt to address these challenges. The ERGP, which is broadly aligned to the SDGs, is aimed at improving macroeconomic stability; fostering economic growth and diversification; improving competitiveness; fostering social inclusion; and enhancing governance and security. Recent information, communication and technological advances have led to data -from both conventional and unconventional sources- to be readily available in high volumes and velocity and in a variety of forms, or simply, to a Data Revolution. This paper examines the role of Big Data and Data Revolution in promoting sustainable development in Nigeria, as well the emerging opportunities for Statisticians in this regard. The paper posits that the attainment of the SDGs will be greatly hampered if Statisticians do not ask the right questions; access relevant data information and, crucially, perform deeper analytics around data and information. Statisticians have an important role to play in promoting Nigeria’s sustainable development agenda, but only if they become more entrepreneurial; and adequately master and apply the requisite technical and non-technical skills.

A novel and efficient classifier using spiking neural network

Classification plays a crucial role in big data, especially in e-commerce operations. Deep learning (DL) research has become a new means to provide a better solution to the problem of classification. In this paper, a deep learning spiking neural network model with the objective of building and testing a more biologically plausible network is proposed. The model is simulated using NengoDL simulator along with TensorFlow in a python environment. The performance of the model is evaluated using FashionMNIST dataset owing to its complexity than the existing MNIST dataset. The results provided better performance, and advantages are viewed in terms of area efficiency due to the use of lesser number of neurons, increasingly biologically plausible network and ease of implementation in hardware like FPGA as SNN is involved.