Graph data management and processing systems have been adopted by many companies and organisations, but the gap in their adoption for business intelligence use-cases and analytical tasks is still substantial. Indeed, practitioners are missing guidelines and best practices that can help them in identifying non-trivial applications of graph analytics tools and approaches beyond one-shot operations.

Moreover, we have only recently witnessed the creation of data management tools that are able to map, within themselves, data imported from multiple sources and models. Thus, their performance, capabilities, and limitations when trying to address hybrid transactional and analytical workflows are still largely unexplored. Further, as schema on top of graph data sources is not available, methods for schema-based data partitioning and query optimization are still missing.

This task will explore schema-based partitioning techniques to improve the efficiency of systems processing big PGs. As this is a largely unexplored area already work from schema-based partitioning of semantic graphs will be revised and exploited through this work.