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☰ Research Areas ❤

☰ Publication Lists ✎

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☰ Research Projects ⚙

➤ Medical Data Analysis

Due to the ongoing digital transformation in medicine, large quantities of health-related data will be available in the future. Comprehensive research into this data requires the support of highly efficient systems for data analysis. Especially the focus on an individualized view in the health sector as well as in risk assessments require advanced analysis techniques to reliably evaluate medical data.


ELISE: A Learning and Interoperable, Smart Expert System for Pediatric Intensive Care Medicine (funded by BMG; 2021-2023)

IDA: Intelligent Data Analysis for Health and Chemical Safety (funded by FhG; 2019-2024)

PatientSim: Optimizing Patient Similarity Analysis on modern Hardware (2017-2018; a project in cooperation with the Future Service-Oriented Computing Lab of Hasso Plattner Institut, Potsdam)

➤ NOSQL databases

NOSQL (in the sense of Not Only SQL) is an umbrella term for several different kinds of database systems that use non-relational technologies. Subcategories include object databases, graph databases, XML databases, key-value stores, column stores, extensible record stores and column-family stores. Research in this area investigates new approaches to query optimization and security in these systems.


CloudDBGuard: Implementing Cryptography-based Approaches to Secure Data Management in Cloud Databases (funded by DFG; 2016-2021)

FamilyGuard: Secure data structures and adaptable encryption for column-family databases (funded by DFG; 2014-2016)

NoSQL-Net: Managing Linked Data in NoSQL Stores under Schema Evolution (funded by BMBF; 2014-2015)

➤ Intelligent database systems

Intelligent database systems offer a mechanism for flexibly answering user queries to help the database user get as much information as possible out of the system. Research in this area covers mechanisms like query generalization to find informative answers.


OntQA-Replica: Intelligent Data Replication for Ontology-Based Query Answering (2014-2015; a project in cooperation with the Future Service-Oriented Computing Lab of Hasso Plattner Institut, Potsdam)

CoopQA: Cooperative Query Answering (funded by DAAD; 2010-2011)

➤ Separation of Duties for Cloud Databases

Confidentiality concerns are important in the context of cloud databases. In the CloudDBSOD project, the technique of vertical fragmentation is explored to break sensitive associations between columns of several database tables according to confidentiality constraints. By storing insensitive portions of the database at different non-communicating servers it is possible to overcome confidentiality concerns. In addition, visibility constraints and data dependencies are supported. Moreover, to provide some control over the distribution of columns among different servers, novel closeness constraints are introduced. Finding confidentiality-preserving fragmentations is studied in the context of mathematical optimization for which a corresponding integer linear program has to be solved.


CloudDBSOD: (2016-2018)

preCQE: Preprocessing inference-proof database instances (2009-2010)

cqeMAXSAT: Using SAT solvers to compute inference-proof propositional database instances (2007-2008)

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