big data IEEE PAPERS-2020



Big Data Technology can be defined as a Software-Utility that is designed to Analyse, Process and Extract the information from an extremely complex and large data sets which the Traditional Data Processing Software could never deal with.



Detect and classify zero day Malware efficiently in big data platform
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Malware has long been familiar on the Internet nowadays as one of the most prominent cyber threats. It expands rapidly in volume, velocity and variety, which overcoming the conventional methods used to identify and recognize malware. In order to suit the size and Experimental chemistry and the younger discipline of computational chemistry have always aspired to increase data volume, velocity, and variety. The recent software developments in machine learning, databases and automation and hardware advances in fast co-processors

Big data and business analytics: A research agenda for realizing business value
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As organizations strive to achieve a competitive edge over their rivals, big data and business analytics are now playing an increasingly important role in realizing performance gains (H. Chen, Chiang, Storeya). This has signaled an increased interest in the domain byScience is considered to rest upon four basic pillars. Traditionally, experiment and theory are considered as the first and second of these. Advances in computing power have driven simulation to become the third pillar. The development of large repositories of data soHow can big data help to understand the migration phenomenon In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on threeTaking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science and Altmetric. com, with a

Spatio-temporal models for big data and applications on unconventional production evaluation
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With the abundance of big data in the oil and gas industry, it can be sufficient to treat and solve petroleum engineering problems using data analytics. Modern data analytic techniques, statistical and machine learning algorithms have received widespreadBig data refers to store, manage, analyze, and process efficiently a huge amount of datasets and to distribute it. Recent advancements in big data technologies include data recording, storage, and processing, and now big data is used in the refinery sector for the estimation of

COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions
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Although scholars in management recognise the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding With an exponential increase in the provisioning of multimedia devices over the Internet of Things (IoT), a significant amount of multimedia big data has been generated from different devices located across the globe. Current proposals in the literature mainly focus on scalar

Big Data Classification Techniques: A Systematic Literature
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Big data is very popular now days because knowledge can be easily removed from evaluating large sets of data . In big data the basic problem is resources because big data cannot be handled by static resources. Big data comprises of sensitive data and this data

Market quality approach to IoT Data on blockchain big data
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The Internet of things (IoT) is considered a key driving force of what the Japanese government refers to as Society 5.0, the image of an ideal future society that the Japanese government currently advocates. 1 Society 5.0 is defined as a humancentered society thatEmerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in

MaRe: Processing Big Data with application containers on Apache Spark
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Background Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for dataintensive analyses. However, current MapReduce frameworks offer poor support for reusing existing processing tools in

Visual Languages for Supporting Big Data Analytics Development.
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We present BiDaML ( Big Data Analytics Modeling Languages), an integrated suite of visual languages and supporting tool to help end-users with the engineering of big data analytics solutions. BiDaML, our visual notations suite, comprises six diagrammatic notationsIn the contemporary society a massive amount of data is generated continuously by various means, and they are called Big Data sets. Big Data has potential and limits which need to be understood by statisticians and statistics consumers, therefore it is a challenge to develop

A Novel Method for Computationally Efficacious Linear and Polynomial Regression Analytics of Big Data in Medicine
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Modern Applied Science; Vol. 1 No. 5; ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education A Novel Method for Computationally Efficacious Linear and Polynomial Regression Analytics of Big Data in Medicine Ahmed

Getting a ( big ) databased grip on ideological change. Evidence from Belgian Dutch
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In this paper we introduce a computationally enriched experimental tool designed to investigate language ideology (change). In a free response experiment, 211 respondents returned three adjectives in reaction to the labels for five regional varieties, one ethnicAdvances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need- availability gap by increasing resources for mental health care. One resource considered

The promises of Machine Learning and Big Data in entrepreneurship research
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While the potential data landscape in econometric research is undergoing dramatic changes, terminologies such as Big Data and the associated discipline of Machine Learning (ML) techniques have so far received little attention among econometricians. In

Big Data in Predictive Toxicology
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Andrea-Nicole Richarz, European Commission-Joint Research Centre, Italy The rate and volume of toxicological data generation is continually growing due to novel techniques and software. The amplified pace and capacity of data generation has repercussions forBig data analytics (BDA) is gaining considerable attention from academics and practitioners as a critical enabler of green supply chain management (GSCM). Research on this emerging field is increasing but generally underexplored. This study conducts a systematic The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data . To solve these problems, a group of new

Big DataDriven Materials Science and Its FAIR Data Infrastructure
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This chapter addresses the fourth paradigm of materials research big datadriven materials science. Its concepts and state of the art are described, and its challenges and chances are discussed. For furthering the field, open data and an all-embracing sharing, an efficient data

A Task Aware Fine-Grained Storage Selection Mechanism for In-Memory Big Data Computing Frameworks
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In-memory big data computing, widely used in hot areas such as deep learning and artificial intelligence, can meet the demands of ultra-low latency service and realtime data analysis. However, existing in-memory computing frameworks usually use memory in an aggressive

Big Data and Cloud Computing
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Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processingAtrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathophysiological mechanism, asWhile the use of geographic information systems (GIS) has become commonplace within the discipline of archaeology, the potential of a big data approach to GIS is yet to be fully exploited within historical archaeology. Archaeologists inspired by developments in the

A Context-based Approach for Partitioning Big Data .
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The dataset analyzed using parallel data processing systems, such as Hadoop or Spark, is usually divided into chunks, or splits. The basic partitioning approach uses the amount of bytes (eg, 64 or 128 MB) as splitting technique, without considering the content of the dataStraggler task detection is one of the main challenges in applying MapReduce for parallelizing and distributing large-scale data processing. It is defined as detecting running tasks on weak nodes. Considering two stages in the Map phase (copy, combine) and threeHealth informatics and biomedical computing have introduced the use of computer methods to analyze clinical information and provide tools to assist clinicians during the diagnosis and treatment of diverse clinical conditions. With the amount of information that can be obtainedThe concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts changeEmergency events are unexpected and dangerous situations which the authorities must manage and respond to as quickly as possible. The main objectives of emergency management are to provide human safety and security, and Social Big Data (SBD) offers an

The PAULING FILE project and materials platform for data science: From big data toward materials genome
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One of the oldest initiatives in materials informatics, the PAULING FILE project, is described. It includes the comprehensive database for inorganic crystalline compounds, their atomic structures, intrinsic physical properties and phase diagrams. On top of that, the powerfulThis paper examines the challenging problem of new user cold starts in subset labelled and extremely sparsely labelled big data . We introduce a new Isle of Wight Supply Chain (IWSC) dataset demonstrating these characteristics. We also introduce a new technique addressingABSTRACT A university is expected to continue to develop through various periods of change and innovation to remain at the forefront in the field of information technology, and always produce graduates who are ready to apply and have quality. A universitysBackground Augmented reality, computer vision and other (eg network functions, Internet-of- Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data Big data techniques are increasingly used in marketing and to inform business decisions. The film industry is one area where big data that analyzes social media is being used to predict the popularity of films and to gauge audience interest. This is also an area where the

A Meta-Analysis of Scholarly Research on Corona virus through Big Data Approach
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The purpose of the study is to examine the status of scholarly literature on Corona virus indexed in the big database, the Lens, taking into account, top-cited articles, top contributors, top active country region, most accepted study field and open access status. Relative growth

Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model
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The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of

Big social data approaches in Internet studies: The case of Twitter
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Abstract Well beyond Internet Studies itself, but arguably led by it to a considerable extent, there has been a turn towards computational methods in the study of social and communicative phenomena at large scale. This computational turn has commonly been

Big data and transportation safety: connecting the dots
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ABSTRACT 1 Big data offer solutions for many challenges for transportation safety, providing more data 2 faster, with higher spatial and temporal resolution. However, researchers and practitioners 3 identify biases in big data that need to be explored and

Big Data in Auditing for the Future of Data Driven Fraud Detection
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Industry revolution 4.0 makes people face change, the auditor profession is no exception. Auditors no longer conduct audits using the manual method but use computerized systems such as big data analytics. Our research aims to find out how auditors must change, whenHow to transform the growing medical big data into medical knowledge is a global topic. However, medical data contains a large amount of personal privacy information, especially electronic medical records, gene data and electroencephalography data ; the current

Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science
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Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific

Analyzing Big Data in Intercollegiate Athletics: An Application to Athletic Donor Behavior.
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Intercollegiate athletic departments rely on fundraising for a significant percentage of their overall revenue. Therefore, the retention of donors is a critical issue. In an effort to better understand the factors that influence this important outcome, this study undertakes the firstRandomized controlled clinical trials (RCTs) are at the heart of evidence-based medicine. However, in surgical practice, RCTs remain uncommon. Conducting well-designed RCTs for surgical procedures is often challenged by inadequate recruitment accrual, blinding, or

The Role of Big Data Analytics in the Relationship among the Collaboration Types, Supply Chain Management and Market Performance of Thai Manufacturing
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The current study is carried out to explore role of big data analytics in the relationship among the collaboration types, supply chain management and market performance of Thai manufacturing firms. It has been revealed through literature review that collaboration among

The need for patient adherence standard measures for Big Data .
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Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An importantInvestors who intend to execute large orders have to always a trade-off between price impact and opportunity cost in Big Data . In this study, reinforcement learning (Q-learning) is applied, due to its strength to support an agent, to make the best decision and take suitable

Towards next generation of Manufacturing: Implications of Big Data and Digitalization in the context of Industry 4.0
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Industry 4.0 has come as a consecutive and predicted outcome of the previous industrial periods, recently dubbed Industry 1.0, 2.0 and 3.0 (Pereira and Romero). As an expected outcome, companies were proactively prepared for the transformational potential

Managing reputational risk associated with big data research and development: an interdisciplinary perspective
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Many private and public actors are incentivized by big data technologies: digital tools underpinned by capabilities such as artificial intelligence and machine learning. While many shared value propositions exist about what these technologies afford, public facing concernsObjective To describe the clinical profile of pterygium in patients presenting to a multi-tier ophthalmology hospital network in India. Methods This cross-sectional hospital-based study included 6 843 new patients presenting between and 2019. Patients with a clinical

The making of 21st century doctors of Bhutan; use of artificial intelligence, big data and values appropriate for the new normal in the 21st century
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Big Data and Artificial Intelligence (AI) is an integral part of digital natives or millennials including in our professional life. Many are handicapped without the presence of digital environment. The COVID-19 pandemic has forced both the economic and scientific world to

The Moderating Role of Business Intelligence in the Impact of Big Data on Financial Reports Quality in Jordanian Telecom Companies
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This study aimed at discovering the impact of big data in terms of its dimensions (Variety, Velocity, Volume, and Veracity) on financial reports quality in the present business intelligence in terms of its dimensions (Online Analytical Processing (OLAP), Data Mining

The Study of Multimedia DBMS in Big Data Issues and Challenges
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Today, multimedia database management is the most important for the new generation and specifically in big data issues for instant saving, calling, and modifying operations for many kinds of documents such as text, video, audio, and so on. A huge number of previousBig data research is an umbrella term that characterizes research in many fields. This chapter will focus specifically on big data research tied to the use of social media primarily with a focus on humanities and social science research. Social media as a data sourceBig Data (BD) has turned into a significant research field owing to the dawn of vast quantity of data generated as of various sources like Internet of things (IoT), social media, and also multimedia applications. BD has played an imperative part in numerous decision-makings

Offline-to-Online Service and Big Data Analysis for End-to-end Freight Management System.
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Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data . Moreover, the amount of data generated by daily business activities to be analyzed forBig data analytics has anonymously changed the overall global scenario to discover knowledge trends for future decision making. In general, potential area of big data application tends to be healthcare, where the global burden is to improve patient diagnostic