DSAA encourages submissions on original and significant theoretical research achievements and best practices of data-driven advanced analytics, computing, discovery, machine learning, management, optimization, statistics, and their applications. Typical topics include but are not limited to:
Data science foundations
- Data characteristics and complexities
- Interactions, couplings, relations, and heterogeneities
- Factors, structures, relations and distributions
- Mathematical, probabilistic, and statistical theories and models
- Learning theories, models, and systems
- Information theories for analytics and learning
- Deep analytics and deep learning
- Cognitve, neural and human learning methods
- Intent and insight learning
- Inference, regularization and optimization
Analytics, learning, and optimization
- Heterogeneous and mixed analytics
- Multi-domain/media/modal/source/view/task learning
- NLP, text and document analysis
- Temporal, sequential and geo-spatial analysis
- Graph, tree, group and community analysis
- Web, online and network analysis
- Adaptive, continual, online, stream and real-time analytics
- Distributed, parallel and high-performing analytics
- Large-scale and scalable analytics
- Descriptive, predictive and prescriptive analytics
Infrastructure, management, and processing
- Data pre-processing, sampling and augmentation
- Feature engineering and transformation
- High-performance, parallel and distributed computing
- Analytical system architectures and infrastructure
- Heterogeneous data/information integration, matching and sharing
- Crowdsourcing, cloud and edge computing
- Post-processing and post-mining
- Human-learning interaction and interfaces
- Web, social web and distributed search
- Indexing and query processing
- Information and knowledge retrieval
- Personalized search and recommendation
Evaluation, applications, and tools
- Complexity, efficiency, effectiveness, and scalability
- Quality, fairness, bias, and evaluation
- Social and economic impact and actionability
- Presentation and visualization
- Analytical and visualization languages and toolkit
- Business and government analytics
- Online, mobile, IoT, social, living analysis
- Domain-specific applications
- Anomaly, fraud, exception, change, event and crisis
- Ethics, integrity and regulation
- Security, trust, diversity, and risk
- Privacy-preserving analytics
- Reproducibility, explanation and interpretability