Knowledge Management in the Development of Data-Intensive Systems [Hardcover]

This book explores the application of established software engineering knowledge and practices to developing big data systems, enhanced with dedicated knowledge management during software development.  It looks at explicit knowledge construction and management and system development as a process of social construction of shared knowledge.

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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