Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Acknowledgments
Editors
Contributors
1 Data-Intensive Systems, Knowledge Management, and Software Engineering
1.1 Introduction
1.1.1 Big Data โ What It Is and What It Is Not?
1.1.2 Data Science
1.1.3 Data Mining
1.1.4 Machine Learning and Artificial Intelligence
1.2 Data-Intensive Systems
1.2.1 What Makes a System Data-Intensive?
1.2.2 Cloud Computing
1.2.3 Big Data Architecture
1.3 Knowledge Management
1.3.1 Knowledge Identification
1.3.2 Knowledge Creation
1.3.3 Knowledge Acquisition
1.3.4 Knowledge Organization
1.3.5 Knowledge Distribution
1.3.6 Knowledge Application
1.3.7 Knowledge Adaption
1.4 Relating Data-Intensive Systems, Knowledge Management, and Software Engineering
1.4.1 Relating Knowledge Life Cycle to Software Development Life Cycle
1.4.2 Artificial Intelligence and Software Engineering
1.4.3 Knowledge Repositories
1.5 Management of Software Engineering Knowledge
1.5.1 Software Engineering Challenges in a Data-Intensive World
1.5.2 Communication Practices
1.5.3 Engineering Practices
1.6 Knowledge Management in Software Engineering Processes
1.6.1 Requirements Engineering
1.6.2 Architectural Design
1.6.3 Design Implementation
1.6.4 Verification and Validation
1.6.5 Maintenance and Support
1.6.6 Software Evolution
1.7 Development of Data-Intensive Systems
1.7.1 Software Engineering Challenges
1.7.2 Building and Maintaining Data-Intensive Systems
1.7.2.1 Requirements Engineering
1.7.2.2 Architecture and Design
1.7.2.3 Debugging, Evolution, and Deployment
1.7.2.4 Organizational Aspects and Training
1.7.3 Ensuring Software Quality in Data-Intensive Systems
1.7.4 Software Design Principles for Data-Intensive Systems
1.7.5 Data-Intensive System Development Environments
1.8 Outlook and Future Directions
References
Part I: CONCEPTS AND MODELS
2 Software Artifact Traceability in Big Data Systems
Chapter Points
2.1 Introduction
2.2 Background
2.2.1 Software Requirements Representation
2.2.2 Traceability
2.2.3 Big Data
2.3 Uncertainty in Big Data
2.3.1 Value
2.3.2 Variety
2.3.3 Velocity
2.3.4 Veracity
2.3.5 Volume
2.4 Software Artifacts in the Big Data World
2.5 Automated Traceability Techniques (State of the Art)
2.5.1 Automated Traceability Generation
2.5.2 Semantic Link Discovery and Recovery
2.6 Traceability Adaptation
2.7 Discussion
Acknowledgments
References
3 Architecting Software Model Management and Analytics Framework
3.1 Introduction
3.2 Preliminaries
3.2.1 Big Data Analytics
3.2.2 Architecture Design
3.3 Approach for Deriving Reference Architecture
3.4 Big Data Analytics Feature Model
3.5 Big Data Analytics Reference Architectures
3.5.1 Lambda Architecture
3.5.2 Functional Architecture
3.6 Application Model Analytics Features
3.7 Related Work and Discussion
3.8 Conclusion
References
4 Variability in Data-Intensive Systems: An Architecture Perspective
4.1 Introduction
4.2 Variability in Data-Intensive Systems
4.2.1 How Variability Occurs in Data-Intensive Systems
4.2.2 Types of Variability in Data-Intensive Systems
4.2.3 Variability Management in Data-Intensive Systems
4.2.4 A Business Perspective
4.3 The Role of Architecture in Data-Intensive Systems
4.4 Reference Architectures to Support Data-Intensive Systems
4.5 Service-Oriented Architecture and Cloud Computing
4.6 Serverless Architectures for Data-Intensive Systems
4.7 Ethical Considerations
4.8 Conclusions
References
Part II: KNOWLEDGE DISCOVERY AND MANAGEMENT
5 Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-Intensive Software Systems
5.1 Introduction
5.2 Motivation
5.3 Approach
5.4 High-Level Requirements Capture
5.4.1 Brainstorming
5.4.2 Process Definition
5.5 Design
5.5.1 Data Management
5.5.2 Data Processing
5.6 Deployment
5.7 Tool Support
5.8 Discussion
5.8.1 Experience to Date
5.8.2 Evaluation
5.8.3 Strengths and Limitations
5.9 Summary
Acknowledgment
References
6 Augmented Analytics for Data Mining: A Formal Framework and Methodology
6.1 Introduction
6.2 Specific Aims of Research in Augmented Analytics
6.3 Related Work in Augmented Analytics
6.3.1 Axiomatic System Design
6.3.2 Data Preparation and Data Modeling
6.3.3 Machine Learning for Data Preparation and Data Discovery
6.3.4 Natural Language Processing for Data Preparation and Data Discovery
6.3.5 Business Analytics and Data Analytics
6.4 Proposed Framework and Methodology for Research on Augmented Analytics
6.5 Applications of Augmented Analytics and Conversational Query Tool
6.5.1 Conversational Query Tool Architecture
Acknowledgement
References
7 Mining and Managing Big Data Refactoring for Design Improvement: Are We There Yet?
7.1 Introduction
7.2 Mining and Detection
7.3 Refactoring Documentation
7.4 Refactoring Automation
7.4.1 Refactoring Tools
7.4.2 Lack of Use
7.4.3 Lack of Trust
7.4.4 Behavior Preservation
7.5 Refactoring Recommendation
7.5.1 Structural Relationship
7.5.2 Semantic Relationship
7.5.3 Historical Information
7.6 Refactoring Visualization
7.7 Conclusion
References
8 Knowledge Discovery in Systems of Systems: Observations and Trends
8.1 Introduction
8.2 Overview of the State of the Art on Knowledge Management in SoS
8.3 Data Collection in Systems of Systems
8.4 Data Integration in Systems of Systems
8.5 Knowledge Discovery in Systems of Systems
8.6 Research Agenda
8.7 Final Considerations
Acknowledgments
References
Part III: CLOUD SERVICES FOR DATA-INTENSIVE SYSTEMS
9 The Challenging Landscape of Cloud Monitoring
9.1 Introduction
9.1.1 Cloud Computing and Its Key Features
9.1.2 Cloud Computing Delivery Options
9.1.3 Our Contributions
9.2 Challenges
9.2.1 Cloud-Generated Logs, Their Importance, and Challenges
9.2.1.1 Ensuring the Authenticity, Reliability, and Usability of Collected Logs
9.2.1.2 Trust Among Cloud Participants
9.2.1.3 Log Tampering Prevention/Detection Challenges
9.2.2 Cloud Monitoring Challenges
9.2.2.1 Monitoring Large-Scale Cloud Infrastructure
9.2.2.2 Unique Cloud Characteristics
9.2.2.3 Layered Architecture
9.2.2.4 Access Requirement
9.2.2.5 Billing and Monitoring Bound
9.2.2.6 Diverse Service Delivery Options
9.2.2.7 XaaS and Its Complex Monitoring Requirement
9.2.2.8 Establishing High-Availability Failover Strategies
9.3 Solutions
9.3.1 Examples of Solved Challenges
9.3.2 Proposed Solution for Authenticity, Reliability, and Usability of Cloud-Generated Logs: LcaaS
9.3.2.1 Blockchain as a Log Storage Option
9.3.2.2 LCaaS Technical Details
9.3.2.3 LCaaS Summary
9.3.3 Proposed Solution for Monitoring Large-Scale Cloud Infrastructure: Dogfooding
9.3.3.1 Challenges of Storing and Analyzing Cloud-Generated Logs
9.3.3.2 Dogfooding Technical Details
9.3.3.3 Dogfooding Summary
9.4 Conclusions
References
10 Machine Learning as a Service for Software Application Categorization
10.1 Introduction
10.2 Background and Related Work
10.3 Methodology
10.4 Experimental Results
10.5 Discussion
10.6 Conclusion
References
11 Workflow-as-a-Service Cloud Platform and Deployment of Bioinformatics Workflow Applications
11.1 Introduction
11.2 Related Work
11.3 Prototype of WaaS Cloud Platform
11.3.1 CloudBus Workflow Management System
11.3.2 WaaS Cloud Platform Development
11.3.3 Implementation of Multiple Workflows Scheduling Algorithm
11.4 Case Study
11.4.1 Bioinformatics Applications Workload
11.4.1.1 Identifying Mutational Overlapping Genes
11.4.1.2 Virtual Screening for Drug Discovery
11.4.2 Workload Preparation
11.4.3 Experimental Infrastructure Setup
11.4.4 Results and Analysis
11.4.4.1 More Cost to Gain Faster Execution
11.4.4.2 Budget Met Analysis
11.4.4.3 Makespan Evaluation
11.4.4.4 VM Utilization Analysis
11.5 Conclusions and Future Work
References
Part IV: CASE STUDIES
12 Application-Centric Real-Time Decisions in Practice: Preliminary Findings
12.1 Introduction
12.2 Opportunities and Challenges
12.3 Application-Centric Decision Enablement
12.4 Method
12.5 Initial Experiences
12.5.1 Accounts
12.5.2 Instrumentation and Control
12.5.3 Discovery
12.5.4 Experimentation
12.5.5 Analysis and Training
12.6 Knowledge Management
12.7 Conclusions
References
13 Industrial Evaluation of an Architectural Assumption Documentation Tool: A Case Study
13.1 Introduction
13.1.1 Relation to Our Previous Work on Architectural Assumption and Their Management
13.2 Assumptions in Software Development
13.3 Related Work on AA Documentation
13.3.1 Approaches used for AA Documentation
13.3.2 Tools used for AA Documentation
13.3.3 Relation to Requirements and Architecture
13.4 Architectural Assumptions Manager โ ArAM
13.4.1 Background
13.4.2 ArAM in Detail
13.4.2.1 AA Detail Viewpoint
13.4.2.2 AA Relationship and Tracing Viewpoint
13.4.2.3 AA Evolution Viewpoint
13.4.2.4 Putting It All Together
13.5 Case Study
13.5.1 Goal and Research Questions
13.5.2 Case and Subject Selection
13.5.2.1 Case Description and Units of Analysis
13.5.2.2 Case Study Procedure
13.5.3 Data Collection and Analysis
13.5.4 Pilot Study
13.6 Results
13.6.1 Overview of the Case Study
13.6.2 Results of RQ1
13.6.3 Results of RQ2
13.6.4 Summary of Results of RQs
13.7 Discussion
13.7.1 Interpretation of the Results
13.7.2 Implications for Researchers
13.7.3 Implications for Practitioners
13.8 Threats to Validity
13.9 Conclusions and Future Work
Acknowledgments
Appendix
References
Glossary
Index