Tracks & Subtopics

The topics of interest include but are not limited to the following :

Track 1 :  Models, Methods and Technology

• Big Data Models, Algorithms, and Architectures
• Data mining tools and techniques
• Data indexing, cleaning, transformation, and curation technologies
• Big data processing frameworks and technologies
• Big data services and application development methods and tools
• Big data quality evaluation and assurance technologies
• Big data system reliability, dependability, and availability
• Open-source development and technology for big data
• Big Data as a Service (BDaaS) platform and technologies 
• Modeling and Managing Large Data Systems
• Machine learning algorithms for big data
• Models, algorithms, and technologies for real-time big data services and applications
• Big data infrastructure
• Network protocols and architectures

Track 2 :   Data Integration

• Semi-structured and Unstructured Data
• Foundational theoretical models for big data
• Computational models for big data
• Semantic Data Integration
• Ontology-based Database Design
• Information Integration
• Programming models, theories, and algorithms for big data
• Standards, protocols, and quality assurance for big data

 Track 3 :  IA & Advanced Analysis

• Business Analytics
• Large scale Machine Learning
• Algorithms and systems for big data search and analytics
• Machine learning for big data and based on big data
• Edge Computing Machine Learning
• Predictive analytics and simulation
• Visualization systems for big data
• Knowledge extraction, discovery, analysis, and presentation 
• Graph Convolutional Networks
• Generative Adversarial Networks
• Multi-modal Learning
• Web Intelligence
• Mobile Computing and Intelligence 

 Track 4 : Optimisation of Big Data Systems

• Computational optimization. 
• Optimization for representation learning. 
• Optimization under Uncertainty
• Multi-objective optimization. 
• Optimization and Game Theory. 
• Surrogate-assisted Optimization. 
• Derivative-free Optimization
• Optimization algorithms for Real-World Applications.
• Optimization for Big Data. 
• Optimization and Machine Learning
• Swarm Intelligence and Optimization
• High Performance Computing
• Parallelization/Performance/Scalability

  Track 5 : Security & Privacy in Big Data Systems

• Big Data Integrity and Privacy
• Access control and authorization
• Blockchain technologies
• Distributed systems security
• Economics of security and privacy
• Intrusion detection and prevention
• Security and privacy for the Internet of Things
• Systems security
• Network security
• Security and privacy policies
• Cloud security
• Models, algorithms and technologies for big data security and integrity
• Practical security and privacy technologies and applications for big data Security and Safety design methods
• Security verification
• Security analysis
• IoT lifecycle management

 Track 6 : Use cases & Industrial Applications

• Big Data Applications in Science, Internet, Finance, Social sciences, Physical sciences, Engineering, life sciences, Telecommunictions, Business, Medicine, Healthcare, Government, Transportation, Industry, Manufacture, etc
• Big data services and applications for healthcare
• Experiences, practices and case studies of big data technologies for healthcare, Transportation, Industry, etc
• Applications of Big Data Technology in the Transport Industry 
• Experiences, practices and case studies of real-time big data services and applications

 Track 7 : Big Data Benchmarking

• Big data workload characterization and benchmarking
• Data generators or reinforcement learning environments 
• Advanced practices in data collection and curation
• Evaluation, simulation, and test of complex systems
• Evaluation methodology and tools
• Simulation technologies and simulators
• Software and hardware simulation
• Benchmarking for AI, big data, blockchain, and cloud computing
• Benchmarking for energy efficiency
• Benchmarking confidence
• Tools in benchmark, measurement, and optimization