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 Analysis

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 data sets

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