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.
Chapter 1: Data-Intensive Systems, Knowledge Management, and Software Engineering
Bruce Maxim, Matthias Galster, Ivan Mistrik, and Bedir Tekinerdogan
PART I: CONCEPTS AND MODELS
Chapter 2: Software Artifact Traceability in Big Data Systems
Erik M. Fredericks and Kate M. Bowers
Chapter 3: Architecting Software Model Management and Analytics Framework
Bedir Tekinerdogan, Cagatay Catal, and Önder Babur
Chapter 4: Variability in Data-Intensive Systems from an Architecture Perspective
Matthias Galster, Bruce Maxim, Ivan, Mistrik, and Bedir Tekinerdogan
PART II: KNOWLEDGE DISCOVERY AND MANAGEMENT
Chapter 5: Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-intensive Software Systems
John Grundy, Hourieh Khalajzadeh, Andrew Simmons, Humphrey O. Obie, Mohamed Abdelrazek, John Hosking, and Qiang He
Chapter 6: Augmented Analytics for Datamining: A Formal Framework and Methodology
Charu Chandra, Vijayaraja Thiruvengadam, and Amber MacKenzie
Chapter 7: Mining and Managing Big Data Refactoring for Design Improvement. Are We There Yet?
Eman Alomar, Mohamed Wiem Mkaouer, and Ali Ouni
Chapter 8: Knowledge Discovery in Systems-of-Systems: Observations and Trends
Bruno Sena, Frank José Affonso, Thiago Bianchi, Pedro Henrique Dias Valle, Daniel Feitosa, and Elisa Yumi Nakagawa
PART III: CLOUD SERVICES FOR DATA-INTENSIVE SYSTEMS
Chapter 9: The Challenging Landscape of Cloud-Monitoring
William Pourmajidi, Lei Zhang, Andriy Miranskyy, Tony Erwin, David Godwin, and John Steinbacher
Chapter 10: Machine Learning as a Service for Software Application Categorization
Cagatay Catal, Besme Elnaccar, Ozge Colakoglu, and Bedir Tekinerdogan
Chapter 11: Workflow-as-a-Service Cloud Platform and Deployment of Bioinformatics Workflow Applications
Muhammad Hafizhuddin Hilman, Maria Alejandra Rodriguez, and Rajkumar Buyya
PART IV: CASE STUDIES
Chapter 12: Application-Centric Real-Time Decisions in Practice: Preliminary Findings
Patrick Tendick, Audris Mockus, and Wen-Hua Ju
Chapter 13: Industrial Evaluation of An Architectural Assumption Documentation Tool: A Case Study
Chen Yang, Peng Liang, Paris Avgeriou, Tianqing Liu, and Zhuang Xiong