Knowledge Management In The Development Of Data Intensive Systems

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

The idea of managing and transforming tacit to explicit knowledge is getting more and more attention in public systems domain. It has been quite sometime that authors, researchers and managers have come to realize that employees, processes and systems of decision-making in the organizations are a great reservoir of tacit knowledge. It is an important challenge to build and manage systems that can capture, store, retrieve and build new knowledge base for effective decision-making and yet have a human interface. This book is an eye opener for people having interest in knowledge management and knowledge management systems in modern organizations. This book covers ideas, models, conceptual papers and case studies covering the whole globe through the lenses of authors of different continents. For good governance and effective management of public systems, the authors have developed knowledge management processes, models and systems that can have universal appeal and applicability. The book has sixteen, well researched, thought provoking papers and case studies from India, Europe, Brazil and USA. The judicious mix of conceptual papers and case studies will help the students/managers to understand and internalize the process and stages of knowledge management from different countries. It will also make them visualize the practice of knowledge management across the diverse organizations and countries.

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

Knowledge management promises concepts and instruments that help organizations support knowledge creation, sharing and application. This book offers a comprehensive account of the many facets, concepts and theories that have influenced knowledge management and integrates them into a framework consisting of strategy, organization, systems and economics guiding the design of successful initiatives. The third edition extends coverage of the two pillars of implementing knowledge management initiatives, organization and systems.

This volume presents a methodology for defining, measuring and improving data quality. It lays out an economic framework for understanding the value of data quality, then outlines data quality rules and domain- and mapping-based approaches to consolidating enterprise knowledge.

Within the past ten years, tremendous innovations have been brought forth in information technology and knowledge management. Some of the key technical innovations have included the introduction of social media, artificial intelligence, as well as improved network connectivity and capacity. Effective Knowledge Management Systems in Modern Society is a critical scholarly resource that presents an overview of how technical, social, and process changes are impacting the way knowledge systems are being designed. Featuring coverage on a broad range of topics such as knowledge engineering, cognitive ergonomics, and interorganizational knowledge, this book is geared toward consultants, practitioners, and researchers seeking current research on how new approaches in knowledge management impact information technology professionals.

The world is moving into a new era of the knowledge economy. In the past decade, the significance of developing knowledge has grown to a level where it is now dominating other socio-economic factors. Systems Approaches to Knowledge Management, Transfer, and Resource Development provides a new view of knowledge management through the lens of systems approach, which looks at each part of the knowledge management system as a section of the full overview. This cutting-edge resource will be essential for academicians, scientists, practitioners, and industry professionals as all of these individuals work toward a new understanding of knowledge and information management practices in the 21st century.

The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

"This book focuses on the challenges of distributed systems imposed by the data intensive applications, and on the different state-of-the-art solutions proposed to overcome these challenges"--Provided by publisher.

Recent research shows that collaboration and social networking foster knowledge sharing and innovation by sparking new connections, ideas, and practices. Yet these informal networks are often misunderstood and poorly managed. Building on the groundbreaking, bestselling first edition, Knowledge Management Handbook: Collaboration and Social Networkin