Knowledge Management in the Development of Data-Intensive Systems

by Ivan Mistrik (Editor), Matthias Galster (Editor), Bruce R. Maxim (Editor), Bedir Tekinerdogan (Editor)

Overview

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.

Product Details

ISBN-13:	9781032015972
Publisher:	CRC Press
Publication date:	09/25/2023
Pages:	342
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

William G. Doerner is a retired Professor of Criminology and Criminal Justice at Florida State University, where he served since 1977. A specialist in victimology and law enforcement issues, he holds a Ph.D. in Sociology from the University of Tennessee. Doerner retired from active duty with the Tallahassee Police Department after 29 years of service as a part-time sworn law enforcement officer. He served on the Board of Directors for the National Organization of Victim Assistance and was the Founding President of the Florida Network of Victim/Witness Services, past Director of the Program in Criminal Justice at Florida State University, and a previous editor of the American Journal of Criminal Justice. In addition to other professional accolades, Doerner received the Outstanding Educator of the Year Award from the Southern Criminal Justice Association and was a winner of the John P.J. Dussich Award from the American Society of Victimology.

Steven P. Lab is Professor of Criminal Justice at Bowling Green State University. He holds a Ph.D. in Criminology from the Florida State University School of Criminology and Criminal Justice. Lab is the author or co-author of five books, co-editor of one encyclopedia, and the author of more than 50 articles or book chapters. He is a past editor of the Journal of Crime and Justice. Lab has been a visiting professor at the Jill Dando Institute of Crime Science of the University College London and at Keele University in Staffordshire, England, as well as a Visiting Fellow at Loughborough University (England) and a Research Consultant with the Perpetuity Research Group at Leicester University (England). Lab has received grant funding for several large research projects from the National Institute of Justice and has served as a consultant to the Ohio Attorney General's Office, the Arizona Governor's Office, and various offices of the U.S. Department of Justice. Lab is also a past president of the Academy of Criminal Justice Sciences.

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.