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.

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