Smart Data 2016 (IEEE International Conference on Smart Data) is an academic conference held in Chengdu, China on 2016-12-15. The paper submission deadline is 2016-09-30. Acceptance notifications are sent on 2016-10-31.
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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