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Table of Contents
Intro
Preface
Author biographies
Haruna Chiroma
Jemal H Abawjy
Chapter 1 Computing disciplines ecosystem and origin of research
1.1 Introduction
1.2 Computing disciplines
1.2.1 Computer science
1.2.2 Software engineering
1.2.3 Computer engineering
1.2.4 Information systems
1.2.5 Information technology
1.2.6 Cybersecurity
1.2.7 Data science
1.2.8 Emerging areas: artificial intelligence
1.3 Comparing the focus of the seven disciplines within the computing ecosystem
1.4 Scientific research in computing
1.4.1 Types of research
1.5 Research philosophy and origin of research in computing
1.5.1 Origin of research in computer science
1.5.2 Origin of research in software engineering
1.5.3 Origin of research in information systems
1.5.4 Origin of research in information technology
1.5.5 Origin of research in data science
1.5.6 Origin of research in cybersecurity
1.5.7 Origin of research in computer engineering
1.6 Comparing research methods in discipline within the computing ecosystems
1.7 Generic procedure for computing research
1.8 Features of quality and bad research
1.9 MSc, PhD and undergraduate project in computing
1.10 Organization of the book
1.11 Summary
References
Chapter 2 Computing search engines and bibliographic databases
2.1 Introduction
2.2 The computing academic databases
2.2.1 Clarivate Analytics Web of Science
2.2.2 Scopus
2.2.3 Comparing the Scopus and Web of Science
2.2.4 DBLP computer science bibliography
2.2.5 ACM digital library
2.2.6 Ei-Compendex
2.2.7 IEEE Xplore
2.2.8 ScienceDirect
2.2.9 Springerlink
2.2.10 PubMed
2.2.11 Google Scholar
2.3 Comparing the academic search engines: similarities and differences
2.4 Summary
References.
Chapter 3 Systematic literature review from a computing perspective and research problem formulation
3.1 Introduction
3.2 Why conduct literature search and systematic literature review?
3.3 Systematic literature review, narrative review and metadata analysis: differences
3.3.1 Approach for conventional review
3.3.2 Synthesis of existing evidence and research problem
3.4 Proposed systematic literature review methodology for computing
3.4.1 Focus and selection of research topic
3.4.2 Motivation
3.4.3 Keywords
3.4.4 Research questions
3.4.5 Literature search and data sources
3.4.6 Study selection procedure
3.4.7 Exclusion and inclusion criteria
3.4.8 Inclusion and exclusion of papers
3.4.9 Screening based on introduction and conclusion (optional)
3.4.10 Stopping the search and the number of articles required for the SLR
3.4.11 Data extraction and synthesis
3.4.12 Answers to research questions
3.4.13 Systematic literature review quality assessment
3.4.14 Discussion
3.4.15 Limitations
3.4.16 Challenges and future research opportunities
3.5 The brief structure of systematic literature review
3.6 Common mistakes in conducting systematic literature review
3.7 Research problem formulation
3.7.1 Developing problem statement
3.7.2 The problem statement-computer science perspective
3.7.3 Research questions
3.7.4 Aim and objectives of the research
3.7.5 Significance of the research
3.7.6 Research scope
3.8 Summary
References
Chapter 4 Computing research tools and resources
4.1 Introduction
4.2 Research productivity tools
4.2.1 Reference management software
4.2.2 EndNote
4.2.3 Plagiarism detection tool
4.2.4 Spelling and grammar checkers: Ginger Software spelling and grammar checker
4.2.5 Google documents
4.2.6 ShareLaTeX.
4.2.7 Backup tools
4.2.8 Finding a suitable journal for submitting a paper
4.3 Computing research tools
4.3.1 Software engineering
4.3.2 Information systems and information technology
4.3.3 Data science
4.3.4 Libraries and frameworks
4.4 Computer engineering
4.4.1 Gem5 simulator
4.4.2 Webots
4.5 Hardware and software platforms
4.6 Summary
References
Chapter 5 Computing datasets and data engineering
5.1 Introduction
5.2 Large-scale datasets as today's reality compared to small-size datasets
5.2.1 Definition of big data
5.2.2 Characteristics of big data
5.2.3 Case studies
5.2.4 Content large-scale data format
5.2.5 Challenges regarding big data
5.3 Benchmark, real-world and synthesis datasets
5.4 Characteristic of real-world datasets
5.4.1 Missing values
5.4.2 Inaccurate values
5.4.3 Imbalanced data
5.4.4 Outliers and noise
5.4.5 Redundant and irrelevant features
5.5 Case study: collecting data from a real physical environment-smart city
5.6 Knowing your dataset
5.7 Cross-datasets
5.8 Dark datasets
5.9 Research data management
5.9.1 Storing, preservation and discarding
5.9.2 Safety, security and confidentiality
5.9.3 Third party access
5.9.4 Data storage facilities
5.9.5 Data retention and publication
5.9.6 Confidentiality management
5.9.7 Responsibilities of researchers on data and developing a data management plan
5.10 Research data repositories and sources
5.10.1 IEEE DataPort
5.10.2 University of California Irvine machine learning repository
5.10.3 Kaggle and Code Ocean
5.10.4 GitHub platform
5.10.5 Google Dataset Search
5.10.6 Quantitative and qualitative means of data collection
5.10.7 Knowledge discovery in database 99-DARPA, ADFA Linux and NSL-KDD
5.11 Features engineering
5.12 Summary
References.
Chapter 6 Methodology from a computing perspective
6.1 Introduction
6.2 Research methodology in computing
6.2.1 Process
6.2.2 Modeling
6.3 Relating the methodology to the computing disciplines
6.4 Algorithms
6.4.1 Algorithm performance analysis
6.5 Solving real-world problems using algorithms
6.5.1 Existing algorithm
6.5.2 Modified algorithm
6.5.3 New algorithm
6.6 Conceptual framework
6.7 Comparative study: hardware and software
6.8 Case studies
6.8.1 Case study 1: software engineering
6.8.2 Case study 2: cybersecurity
6.8.3 Case study 3: cybersecurity
6.8.4 Case study 4: data science
6.8.5 Case study 5: computer science
6.8.6 Case study 6: information systems
6.8.7 Case study 7: computer engineering
6.8.8 Case study 8: information technology
6.9 Summary
References
Chapter 7 Scientific publishing in computing: beginners guide
7.1 Introduction
7.2 PhD/MSc thesis, journal and conference proceedings publications
7.3 Tips for developing an excellent journal paper
7.3.1 Title of the paper
7.3.2 Abstract
7.3.3 Keywords
7.3.4 Introduction
7.3.5 Theoretical background
7.3.6 Methodology
7.3.7 Results and discussion
7.3.8 Conclusions
7.3.9 References
7.4 Common reasons for desk rejection and tips to avoiding it during initial screening
7.4.1 Disregard for journal aims and scope
7.4.2 Poor grammar, writing and formatting
7.4.3 Ethical issue: plagiarism/self-plagiarism
7.4.4 Insufficient contribution or lack of novelty
7.4.5 Resubmitting a rejected paper to a new journal without modification
7.4.6 Duplicate submission
7.4.7 Poorly developed paper
7.4.8 Poor literature reviews
7.4.9 Failure to respond to feedback
7.4.10 Lack of relevance to an international audience
7.5 Cover letter
7.6 Research highlights.
7.6.1 Highlights
7.7 Supplementary materials
7.8 Suggesting reviewers for a researcher's own paper
7.9 Quality measurement in computing publications
7.9.1 Peer review in the research community
7.9.2 Peer review cycle
7.9.3 Types of peer review in the research community
7.10 Responding to reviewer comments
Letter of Response to Reviewer Comments
7.11 Handling of rejections
7.12 Understand different publishing models-open access, subscription, and hybrid
7.12.1 Open access journals
7.12.2 Subscription journals
7.12.3 Hybrid journals
7.13 Summary
References
Chapter 8 Research ethics in computing
8.1 Introduction
8.2 Research ethics from the perspective of computing
8.2.1 Trust
8.2.2 Privacy
8.2.3 Consent
8.2.4 Inclusion and digital divides
8.2.5 Visual ethics
8.2.6 Research ethics using wearable cameras
8.3 The six domains of research ethics
8.4 Research misconduct
8.4.1 Plagiarism
8.4.2 Disciplinary action against plagiarism
8.4.3 Plagiarism types
8.4.4 Authorship
8.4.5 Conflict of interest
8.5 Tips to avoid conflict of interest
8.5.1 Participation in editorial board
8.6 Avoiding research misconduct
8.7 Ethics committees and institutional research boards
8.8 Research ethics in the context of disciplines in computing
8.8.1 Ethics in software engineering
8.8.2 Ethics in computer science
8.8.3 Ethics in information system
8.8.4 Ethics in cyber security
8.8.5 Ethics in data science
8.8.6 Ethics in information technology
8.8.7 Ethics in computer engineering
8.9 Negative research results and tips to get published
8.9.1 Publishing negative results
8.10 Summary
References
Chapter 9 Emerging research trends in computing
9.1 Introduction
9.2 Emerging research topics in computing
9.2.1 Computer science.
9.2.2 Computer engineering.
Preface
Author biographies
Haruna Chiroma
Jemal H Abawjy
Chapter 1 Computing disciplines ecosystem and origin of research
1.1 Introduction
1.2 Computing disciplines
1.2.1 Computer science
1.2.2 Software engineering
1.2.3 Computer engineering
1.2.4 Information systems
1.2.5 Information technology
1.2.6 Cybersecurity
1.2.7 Data science
1.2.8 Emerging areas: artificial intelligence
1.3 Comparing the focus of the seven disciplines within the computing ecosystem
1.4 Scientific research in computing
1.4.1 Types of research
1.5 Research philosophy and origin of research in computing
1.5.1 Origin of research in computer science
1.5.2 Origin of research in software engineering
1.5.3 Origin of research in information systems
1.5.4 Origin of research in information technology
1.5.5 Origin of research in data science
1.5.6 Origin of research in cybersecurity
1.5.7 Origin of research in computer engineering
1.6 Comparing research methods in discipline within the computing ecosystems
1.7 Generic procedure for computing research
1.8 Features of quality and bad research
1.9 MSc, PhD and undergraduate project in computing
1.10 Organization of the book
1.11 Summary
References
Chapter 2 Computing search engines and bibliographic databases
2.1 Introduction
2.2 The computing academic databases
2.2.1 Clarivate Analytics Web of Science
2.2.2 Scopus
2.2.3 Comparing the Scopus and Web of Science
2.2.4 DBLP computer science bibliography
2.2.5 ACM digital library
2.2.6 Ei-Compendex
2.2.7 IEEE Xplore
2.2.8 ScienceDirect
2.2.9 Springerlink
2.2.10 PubMed
2.2.11 Google Scholar
2.3 Comparing the academic search engines: similarities and differences
2.4 Summary
References.
Chapter 3 Systematic literature review from a computing perspective and research problem formulation
3.1 Introduction
3.2 Why conduct literature search and systematic literature review?
3.3 Systematic literature review, narrative review and metadata analysis: differences
3.3.1 Approach for conventional review
3.3.2 Synthesis of existing evidence and research problem
3.4 Proposed systematic literature review methodology for computing
3.4.1 Focus and selection of research topic
3.4.2 Motivation
3.4.3 Keywords
3.4.4 Research questions
3.4.5 Literature search and data sources
3.4.6 Study selection procedure
3.4.7 Exclusion and inclusion criteria
3.4.8 Inclusion and exclusion of papers
3.4.9 Screening based on introduction and conclusion (optional)
3.4.10 Stopping the search and the number of articles required for the SLR
3.4.11 Data extraction and synthesis
3.4.12 Answers to research questions
3.4.13 Systematic literature review quality assessment
3.4.14 Discussion
3.4.15 Limitations
3.4.16 Challenges and future research opportunities
3.5 The brief structure of systematic literature review
3.6 Common mistakes in conducting systematic literature review
3.7 Research problem formulation
3.7.1 Developing problem statement
3.7.2 The problem statement-computer science perspective
3.7.3 Research questions
3.7.4 Aim and objectives of the research
3.7.5 Significance of the research
3.7.6 Research scope
3.8 Summary
References
Chapter 4 Computing research tools and resources
4.1 Introduction
4.2 Research productivity tools
4.2.1 Reference management software
4.2.2 EndNote
4.2.3 Plagiarism detection tool
4.2.4 Spelling and grammar checkers: Ginger Software spelling and grammar checker
4.2.5 Google documents
4.2.6 ShareLaTeX.
4.2.7 Backup tools
4.2.8 Finding a suitable journal for submitting a paper
4.3 Computing research tools
4.3.1 Software engineering
4.3.2 Information systems and information technology
4.3.3 Data science
4.3.4 Libraries and frameworks
4.4 Computer engineering
4.4.1 Gem5 simulator
4.4.2 Webots
4.5 Hardware and software platforms
4.6 Summary
References
Chapter 5 Computing datasets and data engineering
5.1 Introduction
5.2 Large-scale datasets as today's reality compared to small-size datasets
5.2.1 Definition of big data
5.2.2 Characteristics of big data
5.2.3 Case studies
5.2.4 Content large-scale data format
5.2.5 Challenges regarding big data
5.3 Benchmark, real-world and synthesis datasets
5.4 Characteristic of real-world datasets
5.4.1 Missing values
5.4.2 Inaccurate values
5.4.3 Imbalanced data
5.4.4 Outliers and noise
5.4.5 Redundant and irrelevant features
5.5 Case study: collecting data from a real physical environment-smart city
5.6 Knowing your dataset
5.7 Cross-datasets
5.8 Dark datasets
5.9 Research data management
5.9.1 Storing, preservation and discarding
5.9.2 Safety, security and confidentiality
5.9.3 Third party access
5.9.4 Data storage facilities
5.9.5 Data retention and publication
5.9.6 Confidentiality management
5.9.7 Responsibilities of researchers on data and developing a data management plan
5.10 Research data repositories and sources
5.10.1 IEEE DataPort
5.10.2 University of California Irvine machine learning repository
5.10.3 Kaggle and Code Ocean
5.10.4 GitHub platform
5.10.5 Google Dataset Search
5.10.6 Quantitative and qualitative means of data collection
5.10.7 Knowledge discovery in database 99-DARPA, ADFA Linux and NSL-KDD
5.11 Features engineering
5.12 Summary
References.
Chapter 6 Methodology from a computing perspective
6.1 Introduction
6.2 Research methodology in computing
6.2.1 Process
6.2.2 Modeling
6.3 Relating the methodology to the computing disciplines
6.4 Algorithms
6.4.1 Algorithm performance analysis
6.5 Solving real-world problems using algorithms
6.5.1 Existing algorithm
6.5.2 Modified algorithm
6.5.3 New algorithm
6.6 Conceptual framework
6.7 Comparative study: hardware and software
6.8 Case studies
6.8.1 Case study 1: software engineering
6.8.2 Case study 2: cybersecurity
6.8.3 Case study 3: cybersecurity
6.8.4 Case study 4: data science
6.8.5 Case study 5: computer science
6.8.6 Case study 6: information systems
6.8.7 Case study 7: computer engineering
6.8.8 Case study 8: information technology
6.9 Summary
References
Chapter 7 Scientific publishing in computing: beginners guide
7.1 Introduction
7.2 PhD/MSc thesis, journal and conference proceedings publications
7.3 Tips for developing an excellent journal paper
7.3.1 Title of the paper
7.3.2 Abstract
7.3.3 Keywords
7.3.4 Introduction
7.3.5 Theoretical background
7.3.6 Methodology
7.3.7 Results and discussion
7.3.8 Conclusions
7.3.9 References
7.4 Common reasons for desk rejection and tips to avoiding it during initial screening
7.4.1 Disregard for journal aims and scope
7.4.2 Poor grammar, writing and formatting
7.4.3 Ethical issue: plagiarism/self-plagiarism
7.4.4 Insufficient contribution or lack of novelty
7.4.5 Resubmitting a rejected paper to a new journal without modification
7.4.6 Duplicate submission
7.4.7 Poorly developed paper
7.4.8 Poor literature reviews
7.4.9 Failure to respond to feedback
7.4.10 Lack of relevance to an international audience
7.5 Cover letter
7.6 Research highlights.
7.6.1 Highlights
7.7 Supplementary materials
7.8 Suggesting reviewers for a researcher's own paper
7.9 Quality measurement in computing publications
7.9.1 Peer review in the research community
7.9.2 Peer review cycle
7.9.3 Types of peer review in the research community
7.10 Responding to reviewer comments
Letter of Response to Reviewer Comments
7.11 Handling of rejections
7.12 Understand different publishing models-open access, subscription, and hybrid
7.12.1 Open access journals
7.12.2 Subscription journals
7.12.3 Hybrid journals
7.13 Summary
References
Chapter 8 Research ethics in computing
8.1 Introduction
8.2 Research ethics from the perspective of computing
8.2.1 Trust
8.2.2 Privacy
8.2.3 Consent
8.2.4 Inclusion and digital divides
8.2.5 Visual ethics
8.2.6 Research ethics using wearable cameras
8.3 The six domains of research ethics
8.4 Research misconduct
8.4.1 Plagiarism
8.4.2 Disciplinary action against plagiarism
8.4.3 Plagiarism types
8.4.4 Authorship
8.4.5 Conflict of interest
8.5 Tips to avoid conflict of interest
8.5.1 Participation in editorial board
8.6 Avoiding research misconduct
8.7 Ethics committees and institutional research boards
8.8 Research ethics in the context of disciplines in computing
8.8.1 Ethics in software engineering
8.8.2 Ethics in computer science
8.8.3 Ethics in information system
8.8.4 Ethics in cyber security
8.8.5 Ethics in data science
8.8.6 Ethics in information technology
8.8.7 Ethics in computer engineering
8.9 Negative research results and tips to get published
8.9.1 Publishing negative results
8.10 Summary
References
Chapter 9 Emerging research trends in computing
9.1 Introduction
9.2 Emerging research topics in computing
9.2.1 Computer science.
9.2.2 Computer engineering.