The Database (DB) group studies core engineering and foundational challenges in scalable and effective management of Big Data.

Getting more out of Big Data through faster and better insights

Supporting richer, more accurate data analytics with data-intensive systems helps data scientists gain high-value insights for smarter decision-making.

The DB group investigates data management and data-intensive systems, inspired by real-world application and analytics scenarios in close cooperation with public sector and industrial research partners. Expertise within the group includes query language design and foundations, query optimization and evaluation, data analytics, and data integration.

A current research focus in the DB group is on problems in the management of massive graphs, such as social networks, linked open data, financial networks, communication networks, mobility networks, and biological networks. In response to the accelerating growth of graph-structured data collections, the popularity and adoption of graph management systems has increased tremendously in recent years. However, current solutions suffer from poor scalability and efficiency. The DB group is actively developing the fundamental technologies to overcome these limitations in the state of the art.

The DB group impacts the broader community through open-source software development and dissemination of research results. The group is also highly active in industrial R&D collaborations, training and mentoring of early-career scientists, and serving on international efforts such as the LDBC Graph Query Language Standardization Task Force.

Academic and industrial partners include leading research groups in Europe, Asia, and North America. Recent collaborators include Oracle Labs, Neo4j, University of Toronto, National University of Singapore, University of Lyon 1, and TU Dresden. The team has received research grants from the Netherlands Organization for Scientific Research (NWO) and companies such as Oracle Labs USA to support their fundamental research in the field of database systems.

Recent results include:

• G-CORE: A core for future graph query languages. SIGMOD 2018.
• Tink: A temporal graph analytics library for Apache Flink. WWW 2018.
• Landmark indexing for evaluation of label-constrained reachability queries. SIGMOD 2017.
• gMark: schema-driven generation of graphs and queries. IEEE TKDE 2017.
• Clustering-structure representative sampling from graph streams. COMPLEX NETWORKS 2017.
• Query planning for evaluating SPARQL property paths. SIGMOD 2016.