Calls for Papers (special): Journal of Database Management (JDM)


Special Issue On: Artificial Intelligence, Machine Learning, Automation, and Digital Transformation

Submission Due Date
8/1/2020

Guest Editors
John Erickson, Department of Management, University of Nebraska Omaha,
Keng Siau, Department of Business and Information Technology, Missouri University of Science and Technology, US

Introduction
Artificial Intelligence (AI) is assuming increasing importance in role business and our everyday life. The advancement in the areas of machine learning, automation, robotics, analytics, statistical analyses, and a variety of AI-related topics will impact the future of work and the future of humanity. This special issue provides a venue for researchers to publish their works in basic AI discovery investigations, applied research investigating the use of AI in business, and tool or platform development associated with large data sets for machine learning or other AI-related areas.

Recommended Topics
• AI and ML in business applications
• AL and ML applications in FINTECH
• AL and ML applications in Natural Language Processing (NLP)
• AL and ML applications in business analytics and data science
• AL and ML applications in cybersecurity and information assurance
• Al, ML, and Digital Transformation
• Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI)
• Automation
• Chatbots
• Deep Learning
• Digital Ecosystem
• Digital Transformation
• Future of work
• Future of humanity
• Economic impact of AL, ML, and Automation
• Ethical and Moral Issues related to AI
• Industry 4.0
• Machine Learning
• Robotics
• Robotic Process Automation (RPA)
• Supervised and Unsupervised Learning

Submission Procedure
Researchers and practitioners are invited to submit papers to this special theme issue on Artificial Intelligence, Machine Learning, Automation, and Digital Transformation on or before August 1, 2020. The aim is to have the special issue published in 2021.

All submissions must be original and may not be under review at another publication. Interested authors should consult the journal’s guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Professor Keng Siau
Dr. John Erickson Jr.
Guest Editors
Journal of Database Management (JDM)
E-mail: jdm@mst.edu; johnerickson@unomaha.edu

Special Issue On: Knowledge Graph and Deep Learning

Submission Due Date
8/15/2020

Guest Editors
Huanhuan Chen,
University of Science and Technology of China, China

Guanfeng Liu,
Macquarie University, Australia

Lei Li,
Hefei University of Technology, China

Introduction
The knowledge graph describes the concepts, entities, and relationships in the objective world with a structured form. It expresses the information of the Internet in a form closer to the human cognitive world, which provides an excellent ability to organize, manage, and reconcile the massive information of the Internet. Knowledge graph brings vitality to semantic search, and at the same time, it shows the significant power in intelligent Q & A. These make knowledge graph become the infrastructure of Internet knowledge-driven applications. Knowledge graph, together with big data and deep learning, has become one of the core driving forces to promote the development of Internet artificial intelligence.

Objective
This special issue provides a premier international forum for the presentation of original research results in opportunities and challenges about Knowledge Graph, as well as exchange and dissemination of innovative, practical development experiences. The special issue covers all aspects of Knowledge Graph, including algorithms, software, platforms, and applications for knowledge graph construction, maintenance, and inference. The special issue draws researchers and application developers from a wide range of Knowledge Graph related areas such as knowledge engineering, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high-performance computing, and World Wide Web. By promoting novel, high-quality research findings, and innovative solutions to challenging Knowledge Graph problems, the special issue seeks to advance the state-of-the-art in Knowledge Graph research.

Recommended Topics
• Foundations, algorithms, models, and theory of Knowledge Graph processing
• Machine learning, data mining, and statistical methods for Knowledge Graph science and engineering
• Acquisition, representation, and evolution of fragmented knowledge
• Knowledge graphs and knowledge maps
• Knowledge graph security, privacy and trust
• Knowledge graphs and IoT data streams
• Geospatial knowledge graphs
• Ontologies and reasoning
• Visualization, personalization, and recommendation of Knowledge Graph navigation and interaction
• Knowledge Graph systems and platforms, and their efficiency, scalability, and privacy
• Applications and services of Knowledge Graph in all domains, including web, medicine, education, healthcare, and business
• Crowdsourcing, deep learning, and edge computing for graph mining
• Rule and relationship discovery in knowledge graph computing
• Trade-offs between completeness and correctness in Knowledge Graph
• Knowledge Graph refinement approaches
• Knowledge Graph entity set expansion methods
• Knowledge Graph evaluation methodologies and computational performance
• Scalability of Knowledge Graph

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Knowledge Graph and Deep Learning on or before August 15, 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow the APA style for reference citations.

All inquiries should be directed to the attention of:
Huanhuan Chen,
hchen@ustc.edu.cn

Guanfeng Liu,
guanfeng.liu@mq.edu.au

Lei Li
lilei@hfut.edu.cn
Guest Editors
Journal of Database Management (JDM)