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