Knowledge Management in the Development of Data-Intensive Systems
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Knowledge Management in the Development of Data-Intensive Systems

Knowledge Management in the Development of Data-Intensive Systems

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About the Book

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.

Table of Contents:
Chapter 1: Data-Intensive Systems, Knowledge Management, and Software Engineering. PART I: CONCEPTS AND MODELS. Chapter 2: Software Artifact Traceability in Big Data Systems. Chapter 3: Architecting Software Model Management and Analytics Framework. Chapter 4: Variability in Data-Intensive Systems from an Architecture Perspective. PART II: KNOWLEDGE DISCOVERY AND MANAGEMENT. Chapter 5: Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-Intensive Software Systems. Chapter 6: Augmented Analytics for Datamining: A Formal Framework and Methodology. Chapter 7: Mining and Managing Big Data Refactoring for Design Improvement. Are We There Yet?. Chapter 8: Knowledge Discovery in Systems-of-Systems: Observations and Trends. PART III: CLOUD SERVICES FOR DATA-INTENSIVE SYSTEMS. Chapter 9: The Challenging Landscape of Cloud-Monitoring. Chapter 10: Machine Learning as a Service for Software Application Categorization. Chapter 11: Workflow-as-a-Service Cloud Platform and Deployment of Bioinformatics Workflow Applications. PART IV: CASE STUDIES. Chapter 12: Instrumentation and Control for Real Time Decisions in Software Applications: Findings and Knowledge Management Considerations. Chapter 13: Industrial Evaluation of An Architectural Assumption Documentation Tool: A Case Study.


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Product Details
  • ISBN-13: 9781032015972
  • Publisher: Taylor & Francis Ltd
  • Binding: Paperback
  • Language: English
  • Weight: 643 gr
  • ISBN-10: 1032015977
  • Publisher Date: 25 Sep 2023
  • Height: 254 mm
  • No of Pages: 312
  • Width: 178 mm


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Knowledge Management in the Development of Data-Intensive Systems
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Knowledge Management in the Development of Data-Intensive Systems
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