Events/ Big data analytics-as-a-Service 2017: Architecture, Algorithms, and Applications in Health Informatics

August 14th, 2017
Halifax, Canada
May 21st, 2017
Visit Bigdas 2017 website

The objective of the bigdas@KDD2017 is to provide a professional forum for health informatics practitioners, machine learning applied researchers, data scientists, data analytics and Business Intelligence individual's, big data and machine learning-as-a-service providers and customers, amongst data mining researchers working on novel big data machine learning strategies, modeling, and scientific visualization to present their latest research findings, innovations, and developments in turning big data health care analytics into fast, easy-to-use, scalable, and highly available services over the Internet. This workshop 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 health informatics. It will highlight current trends and insights for the future of health data analytics, which is bigger and smarter.

Important Dates

The submission schedule is as follows: All dates are in international date format, i.e. YYYY-MM-DD

  • 2017-05-21: (11:59 PM Pacific Standard Time) Paper submissions
  • 2017-05-27: (11:59 PM Pacific Standard Time) (Optional) Supplemental material submissions
  • 2017-06-21: Paper notifications
  • 2017-07-10: Final submissions
  • 2017-08-14: Workshop starts

Topics of interest:

The topics of interest for bigdas 2017 include, but are not limited to:

Big data machine learning algorithms

  • Big data semi-supervised learning, active learning, inductive inference, organizational learning, evolutional learning, transfer learning, manifold learning, 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, Apache Mahout, etc.
  • Performance evaluation or comparative study of Machine Learning as a Service platforms, such as BigML, Microsoft Azure, Amazon Machine Learning, Google Cloud Prediction API, IBM Watson Analytics, etc.
  • 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


  • Keynote 1:
    • Topic: Analysis of Online Health-Related User-Generated Content
    • Speaker: Vagelis Hristidis, University of California, Riverside, USA
  • Keynote 2:
    • Topic: Building Systems for Big Data Analytics: From SQL to Machine Learning and Graph Analysis
    • Speaker: Yuanyuan Tian, IBM Research, USA

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