This is a new and emerging area for the KDD community and we hope this workshop will bring together researchers and interested audiences to explore the open problems, applications, and future directions in Big data analytics-as- a-Service: Architecture, Algorithms, and Applications in Health Informatics. We invite submission of papers explaining innovative research studies on all aspects of big data analytics and its application in health informatics. Work-in-progress papers, demos, experimental studies, open-source developments, and visionary papers are also welcome. Suggested topics include (but are not limited to) the following with the focus of health informatics application area:

• 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

Important Dates:

Paper submissions: May 21, 2017 (11:59 PM Pacific Standard Time)
Supplemental material submissions (Optional): May 27, 2017 (11:59 PM Pacific Standard Time)
Paper notifications: June 21, 2017
Final submissions: July, 10, 2017
Workshop date: August 14, 2017 (Morning 8:00 am to 12:00)