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Prof. Dr. Stefan Decker - Theses


Master
A Semantic Publish-Subscribe System for the Internet of Production - Open
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Lars Gleim, M. Sc.
In the context of this master thesis, the student should design and implement the recently introduced SPARQL Subscription specification, enabling the subscription to SPARQL queries and subsequent Pub-Sub-based semantic realtime information systems for the Internet of Production.
Master
A Smart Routing Algorithm for the Personal Health Train (PHT) - Open
In the context of this master thesis, the student should investigate on different routing heuristics for the PHT in a distributed Machine Learning/Deep Learning setting.
Master
Automatic Provenance Generation for Serverless Computing - Open
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 (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.
Master
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
Bachelor
A Distributed Data Revisioning System for the Internet of Production - Running
Supervised by Prof. Dr. Stefan Decker, Prof. Dr.-Ing. Klaus Wehrle; Advisor(s): Lars Gleim, M. Sc., Jan Pennekamp
In the context of this thesis, the student should implement a distributed data revisioning system, enabling interorganizational data reuse and extension in the Internet of Production and Internet of Things.
Master
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.
Bachelor
Automatic HyperGraphQL Bootstrapping for Triplestores - Running
In the context of this bachelor thesis, the student should employ (existing) SPARQL queries to automatically extract the data schema (Ontology) of a given RDF graph and programmatically convert it to a HyperGraphQL Schema, thus greatly simplifying the deployment of GraphQL API endpoints for RDF Triplestores.
Bachelor
Developing Data Quality Metrics for Power System Modeling - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, 660b75904e3b7e318151bbdc353d734d
Linked data is gaining new attention in the last years because of its natural connection to knowledge-based applications. The quality of decisions depends heavily on the quality of the underlying data, for reasoning such quality reports are mandatory for each decision. The W3Cs Best Practices Working Groups "Data on the Web Best Practices: Data Quality Vocabulary" defines a vocabulary to archive linking results of data quality assessments to linked data. Also, a basic set of quality dimensions and metrics based on the work of Zaveri et al. (https://dx.doi.org/10.3233/SW-150175) are presented. This thesis aims to fill the gaps between the DQV, the definitions by Zaveri et al. and the realization of linked data quality assessments, to fulfil all requirements to link data quality assessments.
Person Publications

Lars Gleim, Jan Pennekamp, Martin Liebenberg, Melanie Buchsbaum, Philipp Niemietz, Simon Knape, Alexander Epple, Simon Storms, Daniel Trauth, Thomas Bergs, Christian Brecher, Stefan Decker, Gerhard Lakemeyer, Klaus Wehrle

FactDAG: Formalizing Data Interoperability in an Internet of Production

IEEE Internet of Things Journal

Md. Rezaul Karim, Md Ashiqur Rahman, Joao Bosco Jares, Stefan Decker, Oya Beyan

A Snapshot Neural Ensemble Method for Cancer Type Prediction Based on Copy Number Variations

Neural Computing and Applications

Oya Beyan, Ananya Choudry, Johan van Soest, Oliver Kohlbacher, Lukas Zimmermann, Holger Stenzhorn, Md. Rezaul Karim, Michel Dumontier, Stefan Decker, Luiz Olavo Bonino da Silva Santos, Andre Dekker

Distributed Analytics on Sensitive Medical Data: The Personal Health Train

Data Intelligence

More publications…