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Introduction

Artificial intelligence and one of its promising areas, machine learning, have been widely used by the research community to turn massive, diverse, and even heterogeneous health care data sources into high quality facts and knowledge, providing leading capabilities to robust pattern discovery. However, the application of machine learning strategies on big and complex medical datasets is computationally expensive, and it consumes a very large amount of logical and physical resources, such as data storage, CPU, and memory. Additionally, and from the implementation perspective, most big data machine learning algorithms are complex, and their implementations are available for few environments. These operational restrictions cause various difficulties for utilization of big data analytics, and even more, they create challenges to establish novel experiments and develop new research ideas. 

Sophisticated big data analytics-as-a-Service platforms for efficient data analyses is becoming more valuable as the amount of data generated daily in the health care literature exceeds the boundaries of normal processing capabilities. The objective of the bigdas@KDD2017 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 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. 

The first workshop on Big data analytics-as-a-Service: Architecture, Algorithms, and Applications in Health Informatics is taking place on August 14, 2017 (in conjunction with KDD 2017) in Halifax, Nova Scotia, Canada. The workshop will consist of a combination of invited keynote speakers, panel discussion, and paper/poster presentations. We allocate significant time for open discussions on sharing best practices and future directions.

Call for paper

Submission Topics

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

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Important Date

  • Aug 14

    2017

    Conference Date

  • Aug 14 2017

    Registration deadline

Sponsored By
美国计算机学会
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