Conference Information
ICCBD 2020: International Conference on Computing and Big Data
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Conference Date:
Taichung, Taiwan
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Call For Papers
The ICCBD 2020 topics include but are not limited to the following. Authors are encouraged to submit unpublished original research works in one of the following topics.

Artificial Intelligence:

Brain models, Brain mapping, Cognitive science
Natural language processing
Fuzzy logic and soft computing
Expert systems
Decision support systems
Human Computer Interaction
Automated problem solving
Intelligent information systems
Intelligent data mining and farming
Intelligent web-based business
Intelligent networks
Intelligent databases
Intelligent tutoring systems
Distributed AI algorithms and techniques
Distributed AI systems and architectures
Neural networks and applications
Heuristic searching methods
Social impact of AI
Applications (including: computer vision, signal processing, military, surveillance, robotics, medicine, pattern recognition, face recognition, finger print recognition, finance and marketing, stock market, education, emerging applications...)

Data Mining/Machine Learning:

Statistical learning
Hierarchical learning models
Relational learning models
Bayesian methods
Meta learning
Heuristic optimization techniques
Neural networks
Multi-criteria reinforcement learning
Markov chain Monte Carlo (MCMC) methods
Bayesian networks
Time series prediction
Fuzzy logic and learning
Inductive learning and applications
Computational Intelligence
Aspects of natural language processing
Deviation and outlier detection
Explorative and visual data mining
Web mining
Mining text and semi-structured data
Multimedia mining (audio/video)
Artificial neural networks
Legal and social aspects of data mining
Data cleaning and preparation
Missing value imputation
Medicine Data Mining
Business/Corporate/Industrial Data Mining
Data mining and national security
Clustering algorithms used in data mining
Automatic data cleaning
Data visualization
Data reduction methods
Pattern mining
Applications (examples: data mining in education, marketing, finance and financial services, business applications, medicine, bioinformatics, biological sciences, science and technology, industry and government...)
Big Data Infrastructure
Big Data Management
Big Data Search and Mining
Big Data Security and Privacy
Bid Data Applications
Data Engineering
Data Mining
Data Warehousing
Novel Theoretical Models for Big Data
New Computational Models for Big Data
Data and Information Quality for Big Data
New Data Standards
Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
Big Data Analytics in Small Business Enterprises (SMEs)
Big Data Analytics in Government, Public Sector and Society in General
Real-life Case Studies of Value Creation through Big Data Analytics
Big Data as a Service
Big Data Industry Standards
Experiences with Big Data Project Deployments
Novel Theoretical Models for Big Data
Cloud/Grid/Stream Computing for Big Data
High Performance/Parallel Computing Platforms for Big Data
Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
Energy-efficient Computing for Big Data
Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
Software Techniques and Architectures in Cloud/Grid/Stream Computing
Big Data Open Platforms
New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
Software Systems to Support Big Data Computing
Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Big Data Search Architectures, Scalability and Efficiency
Data Acquisition, Integration, Cleaning, and Best Practices
Visualization Analytics for Big Data
Computational Modeling and Data Integration
Large-scale Recommendation Systems and Social Media Systems
Cloud/Grid/Stream Data Mining- Big Velocity Data
Link and Graph Mining
Semantic-based Data Mining and Data Pre-processing
Mobility and Big Data
Multimedia and Multi-structured Data- Big Variety Data
Social Web Search and Mining
Web Search
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Big Data Search Architectures, Scalability and Efficiency
Data Acquisition, Integration, Cleaning, and Best Practices
Visualization Analytics for Big Data
Last updated by Dou Sun in 2020-01-28
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