The Software Principles of Design for Data Modeling, written by Debabrata Samanta of Rochester Institute of Technology in Kosovo, offers a practical and comprehensive solution to the challenges of designing effective software architecture for data modeling. This book covers key topics such as gathering requirements, modeling requirements with use cases, testing the system, building entity-relationship models, building class models in UML with patterns of data modeling and software quality attributes, and use case modeling. It also includes case studies of relational and object-relational database schema design. The unique approach of this book lies in its unifying method for designing software architecture for data modeling. It addresses specific design issues for various types of software systems, including object-oriented, client/server, service-oriented, component-based, real-time, and software product line architectures. With its practical guidance, standard method for modeling requirements and analysis, and comprehensive coverage of key topics and case studies, this book is a must-read for anyone interested in designing effective software architecture for data modeling, whether you are an academic scholar or a professional in the field.

"This book presents cutting-edge research and analysis of the most recent advancements in the fields of database systems and software development"--Provided by publisher.

Amidst the relentless pace of the contemporary business landscape, information technology (IT) projects grapple with an escalating challenge — the need to deliver solutions swiftly, adapt to evolving customer demands, and create value within the ever-shifting dynamics of the market. In this demanding environment, the traditional project management paradigms often fall short, necessitating a shift towards methodologies that embody flexibility, customer collaboration, and iterative development. Herein lies the crux of the issue faced by modern IT projects. Practical Approaches to Agile Project Management is a guide in the tumult of IT project complexities. This book provides a comprehensive solution to the complexities of contemporary project management by delving into topics such as alternative pricing models and the alignment of organizational cultures in IT partnerships, making it an indispensable resource for professionals, academics, and students navigating the domains of business, information technology, or project management. Primarily aimed at IT professionals involved in project management and service delivery, this book caters to a broad spectrum of individuals, including IT Project Managers, Consultants, Entrepreneurs, and Executives at IT Service Firms. Additionally, it extends its value to Business Leaders undertaking IT-enabled transformations and Academic Researchers delving into the intersection of agile methodologies and IT service delivery. From prioritization strategies for IT Project Managers to frameworks for consultants, entrepreneurs, and executives, this book addresses the diverse needs of its readership, offering practical, evidence-based insights to optimize IT service delivery across various organizational contexts.

In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential. Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively.

Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities delves into the transformative potential of artificial and cognitive computing in the realm of healthcare systems, maintaining a specific emphasis on sustainability. By exploring the integration of advanced technologies in smart cities, the authors examine and discuss how AI and cognitive computing can be harnessed to enhance healthcare delivery. The book provides focused navigation through innovative solutions and strategies that contribute to the creation of sustainable healthcare ecosystems within the dynamic environment of smart cities. From optimizing resource utilization to improving patient outcomes, this comprehensive exploration provides insight for readers with an interest in the future of healthcare within the era of intelligent urban development.

In the dynamic landscape of bioinformatics and blockchain technology, a profound challenge is evident: ensuring secure exchange and analysis of complex biological data while maintaining data integrity and ownership. Traditional methods fall short in seamlessly transferring genomic data, spurring the fusion of blockchain innovation and optimization algorithms as a groundbreaking solution. Biology-Inspired Optimization Techniques in Blockchain Systems directly addresses the data integrity and ownership dilemma in bioinformatics and blockchain. Despite the intricacies of genomic data, blockchain's potential solution faces obstacles like data volume and slow transactions. These challenges are adeptly overcome through optimization algorithms. The book, authored by experts in bioinformatics, blockchain, and optimization, offers a comprehensive guide, showcasing how blockchain architecture and biological data intricacies can harmonize. It provides a blueprint for using blockchain to store genomic variants and aligned reads. This work empowers developers, data scientists, and researchers to overcome technological barriers, redefining the landscape of bioinformatics and beyond.

In an era of rapid technological advancements, blockchain technology has emerged as a groundbreaking solution for decentralized trust and immutable record-keeping. Frameworks for Blockchain Standards, Tools, Testbeds, and Platforms delves into the intricate world of blockchain, offering a comprehensive exploration of its various dimensions. At its core, blockchain disrupts the traditional notion of a centralized trusted authority, instead relying on multiple sources of trust and algorithmic consensus for validation. This handbook elucidates the transformative power of blockchain, examining its capacity to offer enduring records that resist modification by trusted or untrusted sources. The concept of mining, akin to decentralized peer review, is demystified in the context of the Bitcoin protocol, which paved the way for subsequent advancements like Ethereum—a decentralized, open-source platform enabling a diverse array of blockchain applications. This book serves as an indispensable guide for various stakeholders, including researchers, academicians, industry practitioners, policy makers, corporate executives, venture capitalists, and entrepreneurs. With its inclusive approach, the book also benefits those indirectly involved in blockchain and its applications. It is equally valuable for undergraduate, graduate, and research-level students seeking to explore the transformative potential of blockchain technology.

Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.

In corporate dynamics, organizations grapple with a pressing dilemma—how to balance the pursuit of success with the well-being and sustained mental health of their workforce. Across industries, employees find themselves burdened with an ever-growing array of responsibilities, a phenomenon that takes a toll on their physical and emotional well-being, ultimately jeopardizing productivity. The pervasive threat of burnout looms large, necessitating a nuanced understanding of the intricate interplay between employee welfare and organizational prosperity. Recognizing the escalating prevalence of mental health issues, particularly in developing nations, scholars are intensifying their focus on the need for comprehensive studies to unearth strategies that can mitigate workplace stress and enhance the overall mental health of employees. This critical exploration forms the crux of our upcoming book, Nurturing Prosperity: Impact of Corporate Social Responsibility on Employee Wellbeing, where we unravel the profound influence of CSR on the multifaceted dimensions of employee wellness. This book presents a comprehensive synthesis of the latest empirical research findings and relevant theoretical frameworks. Tailored for academic scholars, it aims to deepen the understanding of the strategic role of trust in various domains within the information and knowledge society. Encompassing the global economy, networks and organizations, teams and workgroups, information systems, and individual actors in networked environments, the book elucidates how CSR practices can be leveraged to foster employee wellbeing in diverse settings.

UGC NET Computer Science unit-6