Topics of interest include, but are not limited to:

Track 1: Data Science and Its Foundations

  • Foundational Theories for Data Science
  • Data Classification and Taxonomy
  • Data Metrics and Metrology
  • Data inference for Smart/Big Data
  • Theoretical Models for Smart/Big Data

Track 2: Smart/Big Data Infrastructure and Systems

  • Cloud/Cluster/Fog/Edge Computing
  • Parallel Computing for Big Data
  • Open Source Big Data Systems
  • System Architecture and Infrastructure
  • Smart/Big Data Appliance

Track 3: Smart/Big Data Storage and Management

  • Data Collection, Transformation and Transmission
  • Data Integration, Cleaning and Storage
  • Data Query and Indexing Technologies
  • Distributed File/Database Systems
  • NewSQL/NoSQL for Smart/Big Data

Track 4: Smart/Big Data Processing and Analytics

  • Smart/Big Data Search, Mining, Drilling
  • Machine Learning/Deep Learning
  • In-Memory/Streaming/Graph-Based Computing
  • Brain/Nature-Inspired Computing
  • Secure/Privacy-Preserving/Differentially Private Computing
  • New Models, Algorithms and Methods for Smart/Big Data Analytics
  • Visualization Analytics for Smart/Big Data

Track 5: Smart/Big Data Applications

  • Smart/Big Data Applications in All Fields
  • Data as a Service (DaaS)
  • Security, Privacy and Trust Applications in Smart/BigData
  • Smart/Big Data Opening, Sharing, and Trading
  • Practices and Experiences of Smart/ Big Data Project Deployments
  • Ethic Issues in Big/ Smart Data