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Big Data Analytics-as-a-Service: Architecture, Algorithms, and Applications in Health Informatics

Call for Papers

Sophisticated big data analytics-as-a-service platforms for efficient data analyses are becoming more valuable as the data amount daily generated in healthcare domain exceeds the boundaries of normal processing capabilities. The objective of this special issue is to provide a professional forum for data scientists, researchers, and engineers across the world to present their latest research findings, innovations, and developments in turning big data healthcare analytics into fast, easy-to-use, scalable, and highly available services over the Internet.

This special issue is aimed at data science practitioners working at the intersection of big data machine learning, software-as-a-service (SaaS) platforms, Internet of Things (IoT), and healthcare informatics. We invite investigators to contribute original research articles as well as vision papers and descriptions of work-in-progress or clinical studies on benchmark data. A particular interest will be given to papers exploring or discussing insights for the future of health data analytics. This special issue focuses on health informatics application area.

Potential topics include but are not limited to the following:

  • Big data machine learning algorithms
    • Big data semisupervised learning, active learning, inductive inference, organizational learning, evolutional learning, transfer learning, manifold learning, and probabilistic and relational learning
    • Big data deep learning
    • Big data decision support systems
    • Big data scientific visualization
    • Big temporal data mining
    • Big data time series and sequential pattern mining
    • Big data clinical/biomedical text analytics
    • Automatic semantic annotation of medical content
    • Large-scale classification, clustering, and interpretation of biomedical images and videos
    • Genetic data analytics, mining big gene databases, and biological databases
  • Gold standards
    • Feature engineering considerations and selection
    • Algorithm considerations and selection
    • Analysis selection criteria
  • Systems architecture
    • Infrastructures for big data analytics
    • Scalable and high throughput systems for large-scale data analytics
    • Performance evaluation or comparative study of big data analytics tools, such as DataMelt, RapidMiner, Orange, Rattle, Apache Spark MLlib, and Apache Mahout
    • Performance evaluation or comparative study of machine learning as service platforms, such as BigML, Microsoft Azure, Amazon Machine Learning, Google Cloud Prediction API, and IBM Watson Analytics
    • Integration PaaS (iPaaS) supporting big data applications and services
    • Application of cloud computing to big data analytics
  • Big data analytics-as-a-service
    • Big data machine learning-as-a-service
    • Turning big data health informatics into WWW services
    • Big data deep learning-as-a-service
    • Big data infrastructure-as-a-service

Authors can submit their manuscripts through the Manuscript Tracking System at

Submission DeadlineFriday, 30 June 2017
Publication DateNovember 2017

Papers are published upon acceptance, regardless of the Special Issue publication date.

Lead Guest Editor

  • Peggy Peissig, Marshfield Clinic Research Institute, Marshfield, USA

Guest Editors