The research at the Institute of Computer Science and Business Informatics centers around the management of large and complex data sets in economy and society. Our key areas of research include Data Analytics, Artificial Intelligence, Natural Language Processing, Data Integration, IT Security, and Software Development. On this page, you can find an overview of the individual chairs at the institute and a brief description of their research.
Prof. Dr. Simone Paolo Ponzetto
Our research group examines methods for automatic knowledge acquisition and natural language processing (NLP), as well as their application to support empirical research in (computational) social sciences and (digital) humanities. In our work, we investigate a multitude of techniques for understanding texts – ranging from representation learning and distributional semantics all the way to symbolic, entity-based approaches leveraging knowledge graphs – and apply these to a wide spectrum of research topics such as computational semantics, multilinguality, information retrieval, and multimodal NLP, just to name a few.
Prof. Dr. Christian Bizer
The research group for Web-based Systems explores technical and empirical questions concerning the development of global, decentralized information environments. Our current focus is the evolution of the World Wide Web from a medium for the publication of documents into a global data space. Our empirical work accompanies this evolution by monitoring the adaption of semantic markup and linked data technologies on the Web. Our technical work focuses on integrating data from large numbers of Web data sources and includes topics such as information extraction, identity resolution, schema matching, data fusion, and data search. We apply the developed methods for the tasks of integrating product data from large numbers of e-shops as well as for creating large-scale knowledge bases such as DBpedia.
Prof. Dr. Rainer Gemulla
The Chair of Data Analytics has focused its research on systems and methods for analyzing large, complex data sets with the aim of gaining useful knowledge in a both effective and efficient way. Key areas of research include systems for scalable data processing, scalable data mining and machine learning methods, approximation techniques, information extraction and text mining, as well as statistical relational learning methods.
Prof. Dr. Heiner Stuckenschmidt
The chair conducts fundamental and applied research in a wide range of topics pertaining to Artificial Intelligence, including knowledge representation, machine learning, natural language processing and decision theory. The research group is internationally reputed for its work on information integration, the combination of logical and probabilistic reasoning, and human activity recognition. The chair works in close cooperation with the Institute for Enterprise Systems (InES) and has applied AI techniques in a number of projects in sectors such as health care, finance, the automotive industry, and retail. The group has successfully carried out industry-funded projects in collaboration with major companies as well as startups and small technology companies, and is constantly looking for new challenges and opportunities to show the benefits of AI methods in real world applications.
The Chair of Practical Computer Science III (LSPI3) conducts research in the area of database management systems, with an emphasis on query optimization and evaluation. The goal of query optimization is to find the best plan from a given number of execution plans under consideration of costs. The plan execution then looks at the most efficient implementation of the algebraic operators that make up the plan.
The chair conducts research in cryptography and IT security. Our research aim is to develop and apply technical measures that ensure security and privacy for data. More and more often, people and institutions consciously allow outside access to their personal data, for instance by using cloud services. In addition, the overall increased use of embedded systems (Internet of Things) means that many devices are unintentionally fed with certain data, including information about user behavior on smartphones or production processes in the context of industry 4.0. Our goal is to protect the content of these data while maintaining the practicality and benefits of the systems. We also aim to develop new approaches so that such data can be used for more security, for instance as a way of personal authentication.
The chair conducts research in the areas of complexity theory, algorithms and data structure, and cryptography. Key areas of focus currently include the provable security of symmetric encryption methods, hash function constructions and authentication protocols, further development of cryptanalysis techniques, as well as design, analysis and implementation of ultra-lightweight encryption methods for so-called ultra-constrained devices (e.g. RFIDs).
The Chair of Software Engineering examines methods and tools used to efficiently develop dependable software systems. We focus on the integration of different software engineering paradigms at a fundamental level, with a special focus on model-driven development and visualization as well as data mining of comprehensive software repositories (Big Code). The three main areas of research include multi-level (deep) modeling, view-based software engineering, and scalable software analysis and observation.
Prof. Dr. Heiko Paulheim
The focus of the research group is on the development and use of large-scale knowledge graphs. We examine methods for generating these knowledge graphs from various sources (such as wikis and other structured websites) as well as ways to automatically improve the graphs (for instance adding missing information or identifying errors) by means of heuristic inference or machine learning. Furthermore, we take a holistic approach to looking at the construction and refinement of knowledge graphs by attempting to formalize and use meta-knowledge about the process and life cycle of these graphs. In terms of application, we investigate how knowledge graphs can be used to improve the results of different knowledge-intensive tasks.
The focus of this group is the development of innovative programming languages and compilers that enable the programming of portable and high-performance applications for modern computer architectures. We focus in particular on various forms of parallelization. As our computing landscape becomes more and more heterogeneous, such tools are exceedingly important. Our work attaches great importance to abstracting concrete application problems from industry and research such as simulation software with theoretically sound methods and thereby creating higher-level and reusable solutions.
Prof. Dr. Paul Swoboda
Paul Swoboda develops innovative tools for extending the use of machine learning by combining them with other methodologies, mainly optimization. Optimization allows the explicit modeling of constraints on the output of a system and hence offers the possibility to get more inductive biases into machine learning systems. With these tools Paul Swoboda competes on a wide array of basic computer vision tasks, including segmentation, matching and tracking.