Call for papers: Services Computing Management for Artificial Intelligence and Machine Learning

IEEE Transactions on

ENGINEERING MANAGEMENT

Special Issue: Services Computing Management for Artificial Intelligence and Machine Learning

 

Guest Editors

Patrick C. K. Hung

Faculty of Business and Information Technology

University of Ontario Institute of Technology, Canada

Email: patrick.hung@uoit.ca

 

Michael Goul

W. P. Carey School of Business

Arizona State University, USA

Email: Michael.Goul@asu.edu

 

Haluk Demirkan

Milgard School of Business

University of Washington Tacoma, USA

Email: haluk@uw.edu

 

Shih-Chia Huang

Department of Electronic Engineering

National Taipei University of Technology, Taiwan

Email: schuang@ntut.edu.tw

 

Theme

Services computing requires a multi-disciplinary lens that integrates science and technology to bridge the gap between business services and Information Technology (IT) services. Services computing management involves: 1) Ensuring services computing strategy is allied with how the organization manages IT and how IT is aligned with organizational strategy, 2) Designing, building, sourcing and deploying computing solutions that are resilient, trusted, efficient, and address Quality of Service (QoS) expectations, and 3) Overseeing all matters related to business and IT services operations and resources both across business domains and within domains such as finance and healthcare. The pervasive nature of services computing management is exhibited in almost all industry settings. In everyday life, new business service innovations will give rise to an emergent, data-focused economy that will only pick up steam as both consumer and business utilization of Internet of Things (IoT) technologies is advanced. Concomitantly, we are moving towards an era of Artificially Intelligent (AI) services, which are deployed in multi-scale, complex distributed architectures. These AI services can be formed from high-level computational intelligence that leverages emerging analytical techniques associated with Big Data, Web analytics, data and text mining, ontology engineering, semantic web, and many other advances. At the same time, it becomes increasingly important to anticipate technical and practical challenges and to identify best practices learned through experience. In addition, researchers, businesses, and policymakers have seized on Machine Learning (ML) services to support their decisions. ML services will catalyze smart application areas such as drone and robotic computing. ML services will continue to improve with analytics discipline advancements in areas such as data/text mining, predictive analytics, and algorithms that model high-level abstractions in data by using multiple processing layers with complex structures or non-linear transformations. At the same time, the design, development, and deployment of ML services present novel methodological and technological challenges.

The goal of this special issue is to present both innovative and practical solutions to managerial and technical challenges.  In addition, new and compelling service computing technologies are of interest. This special issue will share research and related the practical experience in order to benefit readers, and it will provide clear proof that services computing management is playing an ever-increasing important and critical role in supporting computational intelligence – especially in exciting new cross-discipline research topics in computer science, information systems, and the management sciences. Topics of interest include, but are not limited to:

  • Data Modeling and Implementation
  • Analytics and Algorithms
  • Business Models
  • Delivery, Deployment, and Maintenance
  • Real-time Processing Technologies and Online Transactions
  • Conceptual and Technical Architecture
  • Visualization Technologies
  • Modeling and Implementation
  • Security, Privacy, and Trust
  • Industry Standards and Solution Stacks
  • Provenance Tracking Frameworks and Tools
  • Software Repositories
  • Organizations Best Practices
  • Case Studies (e.g., robotics, healthcare, financial, aviation, etc.)

All submissions must be original with the following requirements: (a) Some innovative component of services computing management for AI and ML, and (b) Related applications in the special issue theme. The paper may not be under review by another publication. Please prepare the manuscript according to IEEE-TEM’s guidelines (http://www.ieee-tems.org/guidelines-for-authors/) and submit it the journal’s Manuscript Central site (https://mc.manuscriptcentral.com/tem-ieee). Please clearly state in the cover letter that the submission is for this special issue.

Submission Schedule

July 28, 2019                     Full paper submission due

September 28, 2019       First-round reviews and initial decision

October 28, 2019             Revised submissions due

December 28, 2019        Final decision

January 28, 2020            Camera-ready version due

IEEE Transactions on Engineering Management is a journal of the Technology and Engineering Management Society of IEEE, published quarterly since 1954. It is dedicated to the publication of peer-reviewed original contributions, by researchers and practitioners, regarding the theory and practice of engineering, technology, and innovation management.

Editor in Chief

Tugrul U Daim, Ph.D. PICMET Fellow

Professor and Director

Technology Management Doctoral Program

Department of Engineering and Technology Management

Maseeh College of Engineering and Computer Science

Portland State University, Portland OR

United States

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