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.
Bachelor, Master
Entwicklung einer Co-Simulationsumgebung zur Abbildung des Prozessdatenverkehrs von intelligenten Energienetzen - Open
Supervised by Prof. Dr. Stefan Decker, Dr-Ing. Michael Andres
Bachelor, Master
Entwicklung eines Systems zur Angriffserkennung in intelligenten Energienetzen - Open
Supervised by Prof. Dr. Stefan Decker, Dr-Ing. Michael Andres
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, Master
Modellierung und Analyse der Anwendbarkeit von Blockchain Technologien in der Energieversorgung - Open
Supervised by Prof. Dr. Stefan Decker, Dr-Ing. Michael Andres
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.
Bachelor
A Framework for Visual Configuration of Application-specific Presentation Views on Standardized Reference Master Data - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Oya Deniz Beyan,
Heiner Oberkampf,
Jan Winkelmann
Master
An Extensible Web Interface for Linked Data Platform Servers - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Lars Gleim, M. Sc.,
Dipl.-Medieninf. István Koren
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
Automated Semantic Type Annotation of Structured Data using Deep learning - Running
Supervised by Prof. Dr. Stefan Decker, Tobias Meisen; Advisor(s):
Lars Gleim, M. Sc.,
André Pomp
Bachelor
Automatic HyperGraphQL Bootstrapping for Triplestores - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Lars Gleim, M. Sc.,
Dipl.-Medieninf. István Koren
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
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Oya Deniz Beyan,
Dr. Christoph Lange
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
Datenanalyse zur Messung der Gleichmäßigkeit des Präparationsauftrags im Schmelzspinnprozess - Running
Supervised by Prof. Dr. Stefan Decker, Thomas Gries; Advisor(s):
Dr. Sandra Geisler,
Lukasz Debicki
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.
Keynote on Inter-Organizational Data Sharing at ER 2019 in Salvador de Bahia/Brazil 