The annual DaWaK conference is a high-quality forum for researchers, practitioners and developers in the field of Big Data Analytics, in a broad sense. The objective is to explore, disseminate and exchange knowledge in this field through scientific and industry talks. The conference covers all aspects of DaWaK research and practice, including data lakes (schema-free repositories), database design (data warehouse design, ER modeling), big data management (tables + text + files), query languages (SQL and beyond), parallel systems technology (Spark, MapReduce, HDFS), theoretical foundations and applications, bringing together active researchers from the database systems, cloud computing, programming languages and data science communities worldwide.

    Theoretical Models for Extended Data Warehouses and Big Data
    Parallel Processing
    Parallel DBMS technology
    Schema-free data repositories
    Modeling diverse big data sources (e.g. text)
    Conceptual Model Foundations for Big Data 
    Query Languages 
    Query processing and Optimization
    Cost Models for advanced optimization
    Semantics for Big Data Intelligence
    Data warehouses, data lakes
    Big Data Storage and Indexing
    Big Data Analytics: algorithms, techniques, and systems
    Big Data Quality and Provenance Control
    Distributed system architectures
    Exploiting hardware to accelerate processing: multicore CPUs, cache memory, GPUs
    Cloud Infrastructure to manage big data
    Scalability and Parallelization using MapReduce, Spark and related systems
    Graph analytics, including social networks and the Internet
    Visualization
    Big Data Search and Discovery
    Big Data Management for Mobile Applications
    Analytics for Unstructured, Semi-structured, and Structured Data
    Analytics for Temporal, Spatial, Spatio-temporal, and Mobile Data
    Analytics for Data Streams and Sensor Data
    Analytics for Big Multimedia Data
    Real-time/Right-time and Event-based Analytics
    Privacy and Security in Analytics
    Big Data Application Deployment
    Pre-processing and data cleaning to build analytic data sets
    Integration of Data Warehousing, OLAP Cubes and Data Mining
    Analytic workflows