Call for Papers


  Download BDML2021 Call for Papers

Prospective authors are invited to submit technical papers of their previously unpublished work. All submissions should be written in English and should have 8 pages at least, including figures, all of them in the standard format pages. Ready versions of accepted papers exceeding Ten (10) pages, up to Twenty (20), will be allowed pending payment of overlength charges (50$/¥300 for each additional page), click here for more fee details. Topics of interest include, but are not limited to the list below.

  • Big Data Analytics
  • Data Science Models and Approaches
  • Algorithms for Big Data
  • Big Data Search and Information Retrieval Techniques
  • Data Mining and Knowledge Discovery Approaches
  • Machine Learning Techniques for Big Data
  • Big Data Acquisition, Integration, Cleaning, and Best Practices
  • Big Data and Deep Learning
  • Scalable Computing Models, Theories, and Algorithms
  • In-Memory Systems and Platforms for Big Data Analytics
  • Big Data and High Performance Computing
  • Cyber-Infrastructure for Big Data
  • Performance Evaluation Reports for Big Data Systems
  • Storage Systems (including file systems, NoSQL, and RDBMS)
  • Resource Management Approaches for Big Data Systems
  • Many-Core Computing and Accelerators
  • Big Data Applications for Internet of Things
  • Mobile Applications of Big Data
  • Big Data Applications for Smart City
  • Healthcare Applications such as Genome Processing and Analytics
  • Scientific Application Case Studies on Cloud Infrastructure
  • Data Streaming Applications
  • Fault Tolerance and Reliability
  • Scalability of Big Data Systems
  • Energy-Efficient Algorithms
  • Big Data Privacy and Security
  • Big Data Archival and Preservation
  • Visual Analytics Algorithms and Foundations
  • Graph and Context Models for Visualization
  • Analytics Reasoning and Sense-making on Big Data
  • Visual Representation and Interaction
  • Big Data Transformation, and Presentation
  • Deep and Reinforcement Learning
  • Pattern Recognition and Classification for Networks
  • Machine Learning for Network Slicing Optimization
  • Machine Learning for 5G system
  • Machine Learning for User Behavior Prediction
  • New Innovative Machine Learning Methods
  • Optimization of Machine Learning Methods
  • Performance Analysis of Machine Learning Algorithms
  • Experimental evaluations of machine learning
  • Data mining in heterogeneous networks
  • Machine learning for multimedia
  • Machine learning for Internet of Things
  • Machine learning for security and protection
  • Distributed and decentralized machine learning algorithms
  • Intelligent cloud-support communications
  • Intelligent ressource allocation
  • Intelligent energy-aware/green communications
  • Intelligent software defined networks
  • Intelligent cooperative networks
  • Intelligent positioning and navigation systems
  • Intelligent wireless communications
  • Intelligent wireless sensor networks
  • Intelligent underwater sensor networks


    •