FDT: Foundations of Database Technologies


Foundations of data management systems come in two flavours. On the one hand there are formal theories based on areas such as logic, discrete mathematics, and probability theory. On the other hand, researchers have developed a wealth of technologies to store, access and manipulate large amounts of information. In its research the group investigates data management applications to identify foundational challenges and to develop both data management technologies and their formal underpinnings. In its research, the group pursues several directions. It aims to provide principled solutions for problems that currently are tackled by ad-hoc approaches. It looks for applications that present foundational challenges, either of conceptual or technological nature, to develop principled solutions and validate them. It also identifies open conceptual or theoretical problems in the data management research community with the aim to solve them. In its teaching, it familiarizes students with the principles underlying data management techniques so that they understand the relationships and interdependencies between existing technologies and will be able to quickly adopt new development.


The individual members have built up research competencies on various foundational topics in databases, which the group combines and focuses.


database theory
database implementation techniques
data quality
semantics of data
data wrangling
knowledge base management
information extraction

Key Technologies

database systems
information integration systems
knowledge bases
computational logics
artificial intelligence

Related Courses

Data Structures and Algorithms
Formal Languages and Compilers
Foundations of Database Systems
Theory of Computing
Seminars in Data and Knowledge Engineering
Distributed Systems
Semantic Technologies
Ontology and Database Systems
Advanced Logic
Data Mining
+ new Data Science courses


Advances in relational database systems,
Models and techniques to maintain data quality,
Techniques to generate and enrich knowledge bases

Industry Partners

Siemens AG, Corporate Technology, Research in Digitalization and Automation, Munich Findologic, Salzburg

Scientific Partners

Jan van den Bussche (U Hasselt)
Angela Bonifati (U Lyon)
Divesh Srivastava (AT&T Research)
Fabian Suchanek (Telecom Paris Tech)
Evgeny Kharlamov (U Oxford)
Pierre Senellart (ENS Paris)
Shazia Zadiq (U Brisbane, Queensland, Australia)
Sebastian Rudolph (TU Dresden)
Paolo Guagliardo (University of Edinburgh)
Ernest Teniente (Universitat Politècnica de Catalunya)
Riccardo Rosati (Università di Roma, La Sapienza)
Peter F. Patel-Schneider (Nuance)
Terry Halpin (University of Queensland)


EnDaQua – Ensuring Data Quality by Business Process Design (Foundation UNIBZ)
CANDy – Completeness-Aware Querying and Navigation on the Web of Data (CRC UNIBZ)
MAGIC – MAnaGing Completeness of Data (Province of Bozen-Bolzano)
The Call for Recall (CRC UNIBZ)
TQTK – The Quest to Know What We Know (CRC UNIBZ)
TaDaQua – Tangible Data Quality (CRC UNIBZ)

Lead by

Werner Nutt


Diego Calvanese
Fariz Darari
Radityo Eko Prasojo
Enrico Franconi
Mouna Kacimi
Olga Kerhet
Elisa Marengo
Marco Montali
Nony Ndefo
Ognjen Savkovic