Call for Papers for Academics
The International Conference on RDAAPS is an annual forum on research in the broadly defined area of data analytics. RDAAPS brings together researchers from academia, industry, and public sector to present and discuss various aspects of data analytics, including privacy, security, and automation. This venue is meant to bring together stakeholders whose interests lie at the interface of these concerns, providing a platform for integrating the needs of industry with the state-of-the-art scientific advancements, and inspiring original research on solving enterprise data challenges.
RDAAPS seeks papers presenting original research in the areas included, but are not limited to:
Big Data Analytics for Decision Making
New models and algorithms for data analytics
Scalable data analytics
Optimization methods in data analytics
Theoretical analysis of data systems
Analytical reasoning systems
Decision making under uncertainty
Learning systems for data analytics
- Large-scale text, speech, image, or graph processing systems
Accountable Data Analytics
Privacy-aware data analytics
Fairness in data analytics
Interpretable and transparent data analytics
Incorporating legal and ethical factors into data analytics
Strings in Data Analytics
Patterns in Big Data
Data compression
Bioinformatics
Algorithms and data structures for string processing
Useful data structures for Big Data
Data structures on secondary storage
Security in Data Analytics
Traceability of decision making
Models for forecasting cyber-attacks and measuring impact
Data usage in mounting security threats
Data analytics for better situational awareness
Domain knowledge modeling and generation
Novel ontology representations
Scalability of domain-based reasoning on Big Data
Modeling and analyzing unstructured datasets
Automation for data analytics, security, and privacy in manufacturing
Application of data analysis in manufacturing
Big Data in Industry 4.0.
Privacy and security in manufacturing
Challenges of automation of data analytic processes
Case studies of the automation of data analytics processes
Architecture for data analytics and security
Built-in privacy and security in data analytics automation