Search results for ""Author Balamurugan Balusamy""

The immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and on semantic changes in future data landscapes.

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AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. ​Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models.

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Digitization of economic and management processes allows for the delivery of new value and higher efficiency in the implementation of strategic goals. This is due to the inclusion of digital technologies in the existing rules of functioning among partners involved in the flow of resources, and from their readiness for digital transformation. A significant challenge, in practical, organizational, and scientific terms is to understand the opportunities and threats resulting from digital transformation, to identify optimal strategies for the development of business entities in new economic and management conditions, taking care to adopt collateral and proper management of new risks. Addressing this challenge to the readers - contributors indicate the latest theoretical advances, and practical examples in Moldova, Kosovo, and the BRIC countries, among others. This results in a synthetic look at the complex digital transformation processes of the modern world, both in terms of the underlying causes and the vast effects of the transformations and digitization of social and economic life. Contemporary Studies in Economic and Financial Analysis publishes a series of current and relevant themed volumes within the fields of economics and finance.

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Big Data Analytics in the Insurance Market is an industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. This book will be a 'must' for people seeking to broaden their knowledge of big data concepts and their real-world applications, particularly in the field of insurance. The insurance industry is largely dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential. Yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. This book an invaluable resource for any insurance professional from practitioners and policymakers working at insurance companies, to undergraduate and graduate students of economics management, and finance. Providing high quality academic research, ESFIRM provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale.

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Big data – unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling and incentivize risk reduction – is a relatively recent development in the insurance industry, the data sets previously being too impossibly great to analyse through traditional methods. However, with the global capacity to collect and store data growing alongside advancements in AI and machine learning technology, insurers need to seriously evaluate their technology stacks to ensure they can remain competitive and respond to growing customer demand. Striking a balance between the technical characteristics of the subject and the practical aspects of decision making, spanning from fraud analytics in claims management, to customer analytics, to risk analytics in solvency, the comprehensive coverage presented makes Big Data an invaluable resource for any insurance professional. Providing high quality academic research, Emerald Studies in Finance, Insurance, and Risk Management provides a platform for authors to explore, analyse and discuss current and new financial models and theories, and engage with innovative research on an international scale. Subjects of interest may include banking, accounting, auditing, compliance, sustainability, behaviour, management, and business economics.

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Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

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