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Scope and Interests

Smart Data aims to filter out the noise and hold the valuable data, which can be effectively used by enterprises and governments for planning, operation, monitoring, control, and intelligent decision making. Although unprecedentedly large amount of sensory data can be collected with the advancement of the cyber-physical-social systems recently. However, having lots of data is not enough. The key is to explore how Big Data can become Smart Data. Advanced Big Data modeling and analytics are indispensable for discovering the underlying structure from retrieved data in order to acquire Smart Data.

Computational Intelligence, a set of nature-inspired computational methodologies and approaches, has advanced in the past decades. A large number of Computational Intelligent technologies such as artificial neural networks, evolutionary computation and fuzzy logic have been developed to address complex real-world problems. The adoption of Computational Intelligence technologies and theories in handling Big Data could offer a number of advantages. Computational Intelligence is considered as an effective tool for harvesting Smart Data from Big Data.

The goal of this symposium is to promote community-wide discussion identifying the Computational Intelligence technologies and theories for Big Data. We seek submissions of papers which invent new techniques, introduce new methodologies, propose new research directions and discuss approaches for unsolved issues.Topics of interest include, but are not limited to:

  • Drill Smart Data from Big Data
  • New Techniques in Smart Data
  • Machine learning algorithms over Big Data
  • Deep learning models, architectures and algorithms for Big Data
  • Brain-inspired representations learning of Big Data
  • High performance computing for Big Data learning
  • Security, privacy and trust in Big Data
  • Streaming data learning
  • Intelligent decision making systems for Big Data
  • Prediction methods for Big Data applications
  • Evolutionary computing in Big Data
  • Swarm Intelligence and Big data
  • Handling uncertainty and incompleteness in Big Data
  • Applications of Fuzzy Set theory, Rough Set theory, and Soft Set theory in Big Data
  • Big Data applications

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