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Smart Data 2016: IEEE International Conference on Smart Data

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Submission Date:
2016-09-30
Notification Date:
2016-10-31
Conference Date:
2016-12-15
Location:
Chengdu, China
Viewed: 14471   Tracked: 3   Attend: 2

Conference Partner Index (CP-I)

43.4 / 100
Ranked #3,608 of 5,693 conferences · Top 64%
Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
22
Public record completeness (15%)
25

Inputs used: Researchers following it here: 3 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-03

Call For Papers

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
Last updated by Dou Sun on

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