Scope

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, database design (data warehouse design, ER modelling), big data management (tables + text + files), query languages (SQL and beyond), parallel systems technology (Spark, MapReduce, HDFS), theoretical foundations and applications, text and data mining techniques, and deep learning. 

Topics of Interest

The conference will bring together active researchers from the database systems, cloud computing, programming languages and data science communities worldwide. Main topics include:

    Theoretical models for extended data warehouses and big data
    Conceptual model foundations for big data
    Modelling diverse big data sources
    Parallel processing
    Parallel DBMS technology
    Distributed system architectures
    Scalability and parallelization using Map-Reduce, Spark, and related system
    Query languages
    Query processing and optimization
    Semantics for big data intelligence
    Data warehouse and data lake architectures
    Pre-processing and data cleaning
    Integration of data warehousing, OLAP cubes, and data mining
    Polystore and multistore architectures
    NoSQL storage systems
    Cloud infrastructures for big data
    Metadata for big data frameworks
    Big data storage and indexing
    Big data analytics: algorithms, techniques, and systems
    Big data quality and provenance
    Big data search and discovery
    Big data management for mobile applications
    AI/ML for Big data analytics (text, time series, spatio-temporal, graph)
    Analytic workflows
    Graph analytics
    Analytics for unstructured, semi-structured, and structured data
    Analytics for temporal, spatial, spatio-temporal, and mobile data
    Analytics for data streams and sensor data
    Real-time/right-time and event-based analytics
    Privacy and security in analytics
    Data visualisation
    Big data application deployment
    Data science products
    Novel applications of text mining for big data
    Machine Learning: auto AI, deep learning applications
    AI/ML for data engineering, management, and analytics