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Informatik 5
Information Systems
Prof. Dr. M. Jarke
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Prof. Dr. Stefan Decker - Theses

Exploring Unknown Environments - Finding Pollution in Underground Pipes - Open
Supervised by Prof. Dr.-Ing. Gerd Ascheid, Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez, Ahmed Hallawa
An Extensible Web Interface for Linked Data Platform Servers - Running
The Linked Data Platform (LDP) is a simple, standardized REST API that allows for the hierarchical organization of Linked Data, similar to a classical file system. While the standard is rather simple, manual interaction with the file system is tiresome and error prone. To simplify the interaction with the LDP server, a suitable web-based user interface for browsing, editing and visualizing LDP data with suitable Web Components should be developed in this work.
Authenticity, Integrity and Trust for Data Reuse in Resource Oriented Architectures - Running
Supervised by Prof. Dr. Stefan Decker, Prof. Dr.-Ing. Klaus Wehrle; Advisor(s): Lars Gleim, M. Sc., Jan Pennekamp
Development of a content security model for authenticity and integrity in resource oriented architectures, based on content signing (public-key cryptography) and hash-chaining/Merkle trees (incorporating the hash/signature of previous revisions and referenced existing resources).
Automatic Provenance Generation for Serverless Computing - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Lars Gleim, M. Sc.
In the context of this work, the student should extend the Function-as-a-Service (FaaS) paradigm of serverless computing with concepts for the automatic tracking of provenance information or data lineage (information about data origins, uses, actors and involved processes over time, similar to a GIT revision history for data) and implement a corresponding framework using a readily available FaaS platform, such as Kubeless, OpenFaaS or the Fn Project, as well as the existing factlib.js library for semi-automatic provenance tracking, that was developed as a result of previous work.
Bachelor, Master
Celsius or Fahrenheit? Interoperable Machine Learning for the Internet of Production Using Semantics - Running
Supervised by Johannes Lipp, M.Sc., Patrick Sapel; Advisor(s): IKV Univ.-Prof. Christian Hopmann (RWTH), Prof. Dr. Stefan Decker
Analyzing a live data integration of an injection molding machine at the IKV, implementing an ontology extraction feature to the existing software (Java, .net o rPython) and adding semantics to the process. Demonstrate the results, e.g. via an ML example.