International Conference on
Big Data, Machine Learning and Applications
(BigDML 2019)

Please click here to submit your paper via EasyChair.

The BigDML solicits outstanding original research papers to be submitted in the following tracks but not limited to:

Track I: Big Data

Track-II: Machine Learning

Track III- Applications

  • Foundational Models for Big Data
  • Active Learning
  • Data Mining
  • Algorithms and Programming Techniques for Big Data Processing
  • Nonlinear Dimensionality Reduction and Manifold Learning
  • Big data in biology
  • Big Data Analytics and Metrics
  • Boosting and Ensemble Methods
  • Recommender Systems
  • Cloud Computing Techniques for Big Data
  • Classification and Clustering
  • Social Network Analysis
  • Big Data as a Service
  • Collaborative Filtering
  • Computer Vision
  • Big Data Open Platforms
  • Components Analysis
  • Image Segmentation
  • Big Data Persistence and Preservation
  • Missing Data
  • Information Retrieval
  • Big Data Quality and Provenance Control
  • Adaptive Data Analysis
  • Matrix and Tensor Factorization
  • Big Data Storage and Retrieval
  • Regression
  • Music Modeling and Analysis
  • Big Data System Security and Integrity
  • Semi-Supervised Learning
  • Video Segmentation
  • Big Data Information Security
  • Spectral Methods
  • Tracking and Motion in Video
  • Privacy Preserving Big Data Analytics
  • Stochastic Methods
  • Audio and Speech Processing
  • Usable Security and Privacy for Big Data
  • Structured Prediction
  • Natural Language Processing
  • Big Data Service Performance Evaluation
  • Unsupervised Learning
  • Natural Scene Statistics
  • Big Data Service Reliability and Availability
  • Link prediction
  • Networking
  • Real-Time Big Data Services
  • Deep Learning
  • Privacy, Anonymity, and Security
  • Usage of Big Data Science for Optimization
  • Optimization
  • AI & Robotics
  • Usage of Optimization for Big Data Science
  • Bayesian Model
  • Signal Processing
  • Big Data Science for Operational Research
  • Belief Propagation
  • Text Analysis
  • Algorithms and Systems for Big Data Search
  • Causal Inference
  • Time Series Analysis