Neeta Nathani, Jagdeesh Kumar Ahirwar · 2024
Manufacturers have been under constant pressure to reduce expenses and boost efficiencies for many years. The increasing cost of raw materials, the desire for reduced pricing from consumers, and the complexity of supply chains have forced firms to look for innovative solutions to comply with these requirements. The need to improve efficiency and agility has become more pressing because to the COVID-19 pandemic. Employing data analytics may benefit manufacturing companies in many ways, such as improved inventory management, more accurate demand projections, improved quality control, and complete end-to-end visibility. After decades of exponential technological progress, commonly known as Industry 4.0, contemporary manufacturers are capable of collecting enormous volumes of data from each and every link in the supply chain, from sourcing raw materials to making the final delivery to the customer. Data platforms can receive real-time data feeds from IoT sensors and edge devices installed on machinery for processing. Sensor-equipped warehouses can collect data from forklift operations that can be utilized to improve operational efficiency. Future product design improvements can be informed by the analysis of data gathered from online consumer reviews, which can help detect quality control issues. Manufacturers have a huge opportunity as a result of the recent tremendous growth in the amount and variety of data available.
Cited by 0Open access
Strategic Direction · 2023
Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Challenges match the potential benefits of big data analytics, and firms must evaluate their staff, infrastructure, and security before introducing the technology. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
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Dr. Sneha Girish Mr. Shrinivas S. Gujarathi Dr. Shubha Rao V Dr. Divyashree B A · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Shihab Hanayneh, Jon Roschke, Sadaf Salek et al. · PDXScholar (Portland State University) · 2016
In the early 2000s, the proliferation of data volume led organizations’ information technology departments to get in a critical situation in terms of data scalability. This was due to the insufficiency of available hardware connected to storage and CPU in dealing with catapulting data volumes. Technology however, bounced back in the following years thanks to Moore’s law [1] leading to the production of faster, smarter, bigger (In terms of capacity) and cheaper CPUs and storage. These breakthroughs in technology enabled companies to embrace the high data volume to their advantage. At the time, the outcomes of technology could be seen anywhere; cellphones and laptops were inseparable from people, and IT systems were being used in back offices in most companies. The focus on information itself however was minimal before that. Decades after computers had become ubiquitous, the accrued data and speed at which it was being produced led areas such as astronomy and genomics to make use of it and turn those quantitative shifts into qualitative ones by gaining new insights [2]. In the same time-frame, the term “Big Data” was coined. Big Data is now being applied to many areas of human endeavor such as Healthcare, Politics, Retail habits, and advertising among many others.
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Mohammad Aaqib, M. Sadiq Ali Khan, D. SATISH CHANDRA · International Journal for Research in Applied Science and Engineering Technology · 2025
Abstract: Decision-makers are inundated with data in the current digital world. Big data presents difficulties for traditional management tools and techniques since it includes datasets that are not only large but also varied and changing quickly. Exploring and putting into practice ways for efficiently managing and extracting insights from large datasets is essential given the growing volume of such data. Decision-makers must extract valuable information from a range of data sources, including social media activity, everyday transactions, and customer contacts. Big data analytics is the process of applying sophisticated analytical methods to big datasets in order to extract insightful information. In addition to discussing the potential advantages of applying big data analytics to a variety of decision-making domains, this paper will examine many analytics methodologies and tools appropriate for big data analysis
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Michael A. Peters · 2017
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Weber, Mike · Fraunhofer-Publica (Fraunhofer-Gesellschaft) · 2019
S.127-140
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Pries, Kim, Dunnigan, Robert · CERN Document Server (European Organization for Nuclear Research) · 2015
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Peter Kieseberg, Lukas Daniel Klausner · OSF Preprints (OSF Preprints) · 2019
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Jaishree Agrawal, Kapish Nautiyal · 2024
Big Data and Big Data Analytics are terms used to describe the enormous amount of information that is produced daily in our contemporary digital environment. The term "big data" refers to massive, complex data volumes that are very challenging to handle and manage successfully when using traditional data processing methods. These data come from a wide range of sources, such as social media, sensors, online transactions, and more. On the other hand, big data analytics is the process of investigating and understanding these enormous databases in order to uncover crucial data, patterns, and trends. People use specialized tools and procedures to make sense of the data and turn it into knowledge that can be used to make better decisions and take the right actions. In this abstract, it is discussed how significant Big Data and Big Data Analytics are in modern culture. We talk about how businesses, governments, and organizations use these technologies to make wise decisions, deliver better services, and boost productivity. Big data analytics has several benefits, including a greater understanding of customer preferences, the identification of potential risks, and the prediction of future trends. Additionally, we go through issues with big data management, such as data privacy, security, and the need for sophisticated computing resources. The ethical concerns surrounding the use of big data are also underlined, with an emphasis on the need for responsible data usage to safeguard individuals' privacy and maintain confidence.
Cited by 2
Mr. P. Ezhumalai, Mr. P. Rajesh, Mr. Santosh Chidambar Deshpande et al. · Zenodo (CERN European Organization for Nuclear Research) · 2025
Big Data Analytics delves deep into the transformative world of data science, offering rich insights into how vast volumes of data are collected, processed, analyzed, and translated into actionable intelligence across industries. As the digital universe expands at a staggering rate—driven by IoT devices, social media, cloud computing, and enterprise systems—the need to extract meaningful patterns and trends from this deluge of information has never been greater. Our platform serves as a vital resource for data professionals, business leaders, researchers, and tech enthusiasts seeking to understand the technologies, methodologies, and real-world applications of big data. From foundational concepts like distributed computing, data warehousing, and parallel processing to advanced topics such as artificial intelligence, deep learning, real-time stream analytics, and predictive modeling, we offer content that is both accessible and technically rigorous. We spotlight major tools and frameworks like Hadoop, Spark, Kafka, Hive, and NoSQL databases, exploring how they are implemented in production environments. The publication also dives into data governance, quality, security, and ethical concerns, ensuring readers stay informed about responsible data practices. Through expert-authored articles, white papers, interviews with industry leaders, and in-depth case studies, we provide a 360-degree view of the data ecosystem. Each feature aims to bridge the gap between data theory and practice, illustrating how data-driven decisions improve business outcomes, customer engagement, and operational efficiency. From optimizing supply chains and forecasting market trends to detecting fraud and personalizing user experiences, big data is at the heart of innovation. Big Data Analytics covers applications across sectors like healthcare, finance, manufacturing, transportation, energy, education, and e-commerce, showing how data fuels smarter solutions in both public and private domains. Whether it's cloud-native data architectures, edge computing, or hybrid data environments, we explore the evolving infrastructure behind scalable analytics. Our audience includes not only experienced data scientists and engineers, but also business strategists, digital transformation officers, and students entering the field. We believe in democratizing data knowledge by making it comprehensive, current, and engaging. With the integration of machine learning and AI into every facet of modern analytics, the future of big data is more dynamic than ever, and our publication is here to chart that journey. As organizations strive for agility, resilience, and competitiveness in a data-first world, understanding how to harness big data becomes a strategic imperative. Big Data Analytics stands as your guide in this landscape—providing clarity, depth, and vision in a domain where information is power.
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Yingxu Wang, Jun Peng · International Journal of Cognitive Informatics and Natural Intelligence · 2017
Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.
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Handbook of statistics · 2015
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Nachiappan Subramanian, Muhammad D. Abdulrahman, Hing Kai Chan et al. · Advances in logistics, operations, and management science book series · 2016
In this chapter, we will introduce practical issues and implementation challenges from the industry perspective. In particular, we explain three aspects based on the panel discussions from the set of representatives participated in a big data conference from three dominant industries such as e-commerce, health care and computer hardware, which are sought of big data for their growth and development. We introduce overall challenges and explain typical industry based practical issues, how they visualize the big picture for their strategic development and how industries are gearing towards converting the challenges to big opportunities through the partnership of universities. Finally, based on the content analysis we offer potential trends and future research directions.
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Muhammad D. Abdulrahman, Nachiappan Subramanian, Hing Kai Chan et al. · Advances in logistics, operations, and management science book series · 2016
This chapter discusses the scholarly views on big data analytics with respect to the challenges in terms of visualization and data driven research in smart cities and ports. The prominent challenges and emerging research on structuring data, data mining algorithms and visualization aspects are shared by academic experts based on their ongoing research experience. Scholars agreed that being able to analyze huge data at once is highly critical for the embracement and success of big data research and the utilization of its findings particularly for entities with highly dynamic and complex demands such as cities and ports. It was noted that developing robust ways of handling and clean qualitative social media data as well as getting well-trained and highly skilled human resources in all aspects of big data analysis and interpretation remains a major challenge.
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Xue, Feiyang, Author · UC Berkeley · 2015
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Amit Kumar, Sheikh F. Ahmad · Indian journal of animal genetics and breeding. · 2025
This paper reviews the methodological advancements, statistical models, and technological platforms underpinning genome-wide association studies (GWAS), highlighting its transformative role in livestock breeding programs. A significant focus is placed on the integration of high-throughput genotyping and phenotyping, linkage disequilibrium models, and multiomics data to enhance genomic prediction and selection accuracy. The study also emphasizes GWAS applications in Indian livestock, particularly Vrindavani cattle and indigenous pig breeds, for traits such as milk composition, mineral content, and immune response to classical swine fever (CSF) vaccination. Despite its strengths, GWAS faces limitations including reliance on existing SNP panels, stringent significance thresholds, and challenges in pinpointing causal variants. The application of mixed models, principal component analysis, and CNV analysis are discussed as strategies to mitigate these issues. The findings underscore GWAS as a pivotal tool for unravelling the genetic architecture of economically important traits, paving the way for precision breeding and improved animal health management. Future prospects lie in exploring gene–gene and gene–environment interactions to bridge the gap of missing heritability and to optimize trait-specific breeding strategies.
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A. Can Inci · 2023
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Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal et al. · 2024
The world has changed in the information space over the last two decades due to three main factors: firstly there has been the routine deployment of high content measurement devices, from personal photos to satellite images to DNA sequencing to social media feeds.Secondly we have had the network and disk to store information at scale, often storing information that we don't know the value of.Thirdly increasingly sophisticated computational techniques, given labels such as "data science," "machine learning," and "AI," have been developed.All these phenomena can be collected under the heading of "Big Data."This book provides an overview of these trends and the practical ways to handle this.Much of the complexity of dealing with data at this scale is about engineering-the practicalities about whether one can manage data flows robustly and cheaply-as well as the more statistically and algorithmically sophisticated analysis schemes.Here the reader can learn about both, and see this from a generic perspective of how to transmit, store, and organise data through to more subjectspecific topics such as an introduction to Big Data approaches in bioinformatics.The book is designed for a broad audience, applicable to seasoned computational and data scientists as well as people at the start of their careers.The authors have provided both overviews and practical examples.
Cited by 14Open access
Xue, Feiyang, Author · UC Berkeley · 2015
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