The complexity of Big Data technologies and the variety of competences required to design applications relying on them has emphasized the relevance of systems for managing and documenting Big Data architectures. Documentation, reconfiguration, and verification are for example crucial tasks for a solid design of technological solutions but are not easily supported in the current landscape of Big Data technologies. Rethinking data and information management in the context of Big Data technologies is then a primary goal for future research.

Methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to this goal. Solutions for integrating and querying schema-less data, for example, have received much attention. Standards for metadata management have been proposed to improve data integration among silos and to make data more discoverable and accessible through heterogeneous infrastructures. A further level of application of Data Semantics principles into Big Data technologies involves Representing Processes, i.e. representing the entire pipeline of technologies connected to achieve a specific solution and make this representation shareable and verifiable to support a mature implementation of the Big Data production cycle.

The symposium will bring together leading researchers, engineers, and scientists from around the world.

Topics of interest for submission include, but are not limited to:

Big Data Management

Metadata Management
Big Data Persistence and Preservation
Big Data Quality and Provenance Control
Big Data Storage and Retrieval

Big Data Integration Architectures and Techniques

Data Source Discovery
Big Data Profiling and Semantics Discovery
Querying Heterogeneous Big Data Repositories
Caching and Materializing Query Results

Quality of Big Data Services

Big Data Service Performance Evaluation
Big Data Service Reliability and Availability
Reproducibility of Big Data Services
Verifiability of Big Data Services
Assurance in Big Data Services

Big Data Visualization

Real Time Visualisation
Visualization Analytics for Big Data
Big Social Media Mining

Big Data Security and Privacy

Big Data System Security and Integrity
Big Data Information Security
Privacy-Preserving Big Data Analytics
Usable Security and Privacy for Big Data

Performance of Big Data Architectures

Query Optimization
Optimal Selection of Analytics
Physical Structures