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.

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.

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.

The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

This book provides practical knowledge on different aspects of information and knowledge management in businesses. For enterprises/businesses those intend to remain prosperous and prolific, it is critically important to share best practices, ensure efficient information flow across company, capturing shared knowledge centrally, and communicate compliance rules, i.e. managing competently information in general. It enables faster and better decisions by helping employees’ to build a strong expertise and by avoiding duplicated projects. Thus, the second volume of this series subline continues to explore different aspects of information and knowledge handling as well as doing business with information. We survey further the key aspects of managerial implications of the informational business. The novel methodologies and practices for the business information processing as well as application of mathematical models to the business analytics and efficient management are examined.

Knowledge: In the realm of knowledge management, information plus wisdom equals knowledge. Organizations have found that the knowledge they contain can be one of their most important competitive weapons Definition: Knowledge management: The ability of an organization to manage, store, value, and distribute knowledge. Some organizations have created the position of Chief Knowledge Manager (CKM) to handle knowledge management responsibilities Many organizations fail to effectively manage and use the most important competitive edge they possess - their knowledge and "intellectual capital." This book covers the entire growing field of knowledge management, with particular emphasis on knowledge-based systems and their use in preserving knowledge in an organization, and integrating it across departments and disciplines. This hands-on guide shows how businesses and other organizations can re-engineer their processes using an applied knowledge-based approach. Each chapter introduces a different aspect of the field and demonstrates its application in actual case studies. Examples from industry, education, and government show the wide application of this exciting new field of study. The book also covers promising trends such as learning organizations, intelligent organizations, and enterprise management.