Skip to content. | Skip to navigation

Informatik 5
Information Systems
Prof. Dr. M. Jarke
Sections
Personal tools
You are here: Home Staff Prof. Dr. Stefan Decker

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
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.
Master
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
Data Quality for Fitness of Use - Running
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.
Bachelor
Dynamic Embeddings of Evolving Knowledge Graphs - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez, Dr. Florian Lemmerich
The goal of this Bachelor thesis is the research of updating KG embeddings with new information in order to obtain a dynamic and stable embedding of the fast-evolving KG while reducing the computational effort.
Master
Graph-Structured Query Construction for Natural Language Questions - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Graph-structured queries provide an efficient means to retrieve desired data from large-scale knowledge graphs. However, it is difficult for non-expert users to write such queries, and users prefer expressing their query intention through natural language questions. Recently, an increasing effort is being exerted to construct graph-structured queries for given natural language questions. At the core of the construction is to deduce the structure of the target query and retrieve vertices/edges of the underlying knowledge graph which constitute the query. Existing query construction methods rely on conventional graph-based algorithms and question understanding techniques, which lead to inefficient and degraded performances facing complicated natural language questions over knowledge graphs with large scales. In this thesis, we focus on this problem and propose novel construction models standing on recent knowledge graph embedding techniques. Extensive experiments were conducted on question answering benchmark datasets, and the results demonstrate that our models outperform baselines in terms of effectiveness and efficiency.
Person Publications

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

Jan Pennekamp, Markus Dahlmanns, Lars Gleim, Stefan Decker, Klaus Wehrle

Security Considerations for Collaborations in an Industrial IoT-based Lab of Labs

Proceedings of the 3rd IEEE Global Conference on Internet of Things (GCIoT '19), December 4–7, 2019, Dubai, United Arab Emirates Publisher: IEEE, December 2019 accepted

More publications…