• P.O. Box 513, Department of Mathematics and Computer Science

    5600 MB Eindhoven

    Netherlands

  • Groene Loper 5, MetaForum

    5612 AP Eindhoven

    Netherlands

Organization profile

Introduction / mission

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

Highlighted phrase

Getting more out of Big Data through faster and better insights

Organisational profile

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.

Fingerprint Dive into the research topics where Database Group is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.

Query languages Engineering & Materials Science
Information management Engineering & Materials Science
Query processing Engineering & Materials Science
Data structures Engineering & Materials Science
Data storage equipment Engineering & Materials Science
Algebra Engineering & Materials Science
XML Engineering & Materials Science
Expressiveness Mathematics

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2009 2020

Comparing the expressiveness of downward fragments of the relation algebra with transitive closure on trees

Hellings, J., Gyssens, M., Wu, Y., van Gucht, D., van den Bussche, J., Vansummeren, S. & Fletcher, G. H. L., Mar 2020, In : Information Systems. 89, 16 p., 101467.

Research output: Contribution to journalArticleAcademicpeer-review

Query languages
Algebra
Data structures
Chemical analysis

An experimental study of context-free path query evaluation methods

Kuijpers, J., Fletcher, G., Yakovets, N. & Lindaaker, T., 23 Jul 2019, Proceedings of the 31st International Conference on Scientific and Statistical Database Management, SSDBM 2019. Malik, T., Maltzahn, C. & Jimenez, I. (eds.). New York: Association for Computing Machinery, Inc, p. 121-132 12 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Context free grammars
Bioinformatics
Labels
1 Downloads (Pure)

Cluster-preserving sampling from fully-dynamic streaming graphs

Zhang, J., Zhu, K., Pei, Y., Fletcher, G. & Pechenizkiy, M., 1 May 2019, In : Information Sciences. 482, p. 279-300 22 p.

Research output: Contribution to journalArticleAcademicpeer-review

Streaming
Sampling
Graph in graph theory
Clustering
Surge

Press / Media

12th edbt summer school on graph data management

George H.L. Fletcher

5/04/15

1 item of Media coverage

Press/Media: Expert Comment

17th international workshop on the web and databases (WEBDB 2014)

George H.L. Fletcher

4/04/14

1 item of Media coverage

Press/Media: Expert Comment

Student theses

A computational biology framework: a data analysis tool to support biomedical engineers in their research

Author: Beishuizen, T., 29 Nov 2018

Supervisor: Bosnacki, D. (Supervisor 1), Cheplygina, V. (Supervisor 2), Hilbers, P. (Supervisor 2), Fletcher, G. (Supervisor 2) & Vanschoren, J. (Supervisor 2)

Student thesis: Master

Artifact-centric log extraction for cloud systems

Author: Santana Calvo, H., 27 Nov 2017

Supervisor: Fahland, D. (Supervisor 1), Serebrenik, A. (Supervisor 2) & Fletcher, G. (Supervisor 2)

Student thesis: Master

File

A streaming graph library fo Apache Flink

Author: Ma, X., 24 Sep 2018

Supervisor: Pechenizkiy, M. (Supervisor 1), Fletcher, G. (Supervisor 2), de Nooij, E. (External person) (External coach) & Teunissen, F. (External person) (External coach)

Student thesis: Master

File