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.
In the event the system is adopted by a portion of the workforce, O&M costs start to rise because it is a ... data-intensive systems with rigid rules that will require a more conventional “waterfall” approach to development.
Mastering data-intensive collaboration and decision making (pp. 49–70). ... Values congruence and differences between the interplay of personal and organizational value systems. ... Journal of Management Development, 27(6), 541–553.
... reference architecture of Big Data systems (in alphabetical order) 1 B. Geerdink, “A Reference Architecture for Big Data Solutions” [11] 2 C. Ballard et al., Information Governance Principles and Practices for a Big Data Landscape.
This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation.
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.
... multimedia representation , multimodal data processing ) Web Information Management : data models for the Web ... self - organized systems modeling and development , hard computational domains , knowledge and data intensive systems ...
The 17 papers from the September 1999 workshop discuss how user interfaces to data intensive systems can be made easier both to construct and to use. In addition to sessions...
These two volumes collect papers presented at the first joint meeting of the two principal logic programming conferences, held in August of 1988. The more than fifty contributions cover all...
Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control.
The book explains how to improve system quality with focus on attributes such as usability, maintainability, flexibility, reliability, reusability, agility, interoperability, performance, and more.