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Prof. Dr. S. Decker
RWTH Aachen
Informatik 5
Ahornstr. 55
D-52056 Aachen
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Running Theses


Bachelor
Reach for the Stars: Gamified Group Work in Mixed Reality
Collaboration and group work are essential parts of learning since they encourage students to exchange ideas, to learn from each other and to support each other. The ability to work as a team increases the quality and amount of results and is therefore a highly demanded soft skill. However, students who face group work in courses still encounter challenges regarding efficient coordination, motivation and communication when working on group assignments or in learning groups. This becomes evident in processes where one person does all the work. Other common examples include a lack of communication where the group accumulates their individual results only at the end of the project and the participants cannot compensate if some members failed to deliver their results on time. Mixed Reality bears the potential to provide a structured 3D environment for organization and collaboration where group tasks can be split up visually between participants in virtual meetings. Additionally, Gamification can provide means of motivation to continue with a learning task and to strengthen the unity of the team.
Bachelor
Agent-Object Interactions in Mixed Reality Learning Applications
Supervised by PD Dr. Ralf Klamma, AOR; Advisor(s): Benedikt Hensen, M.Sc.
Mixed reality agents have a large potential for mixed reality learning applications, e.g. by providing a natural user interface to a chatbot or as a tutor who can demonstrate practical actions and guide the user through a complex environment. When creating such a virtual agent, one challenge concerns the scalability of interactions with the environment. The virtual agent must be able to interact with a large variety of different objects that can be both virtual but also real in the case of augmented reality. Hence, a consistent data description language and a decision-making architecture are required, so that the agent can understand the possible interaction affordances in the environment. Previous approaches from game development e.g. include "smart objects" in the Sims franchise which provide such distributed world knowledge.
Master
Extending the b-it Chain to execute smart contracts
Supervised by Prof. Dr. Thomas Rose; Advisor(s): Thomas Osterland
Often only noticed as a technology that enables the digital currency Bitcoin, blockchain is a novel protocol that allows the distributed and secure storing of information and untempered execution of program code in trust-less environments. Did you ever feel the intense desire to write a thesis about blockchain or do you have a slight hope that blockchain is the one-and-only topic that touches your heart? Use your chance now! We are looking forward to hear from you.
Master
Privacy Attack on Social Networks Using Network Embeddings
Supervised by Prof. Dr. Markus Strohmaier, Prof. Dr. Stefan Decker; Advisor(s): Dr. Florian Lemmerich, Dr. Michael Cochez
Abstract. A company that runs a social network trains a node embedding on the network where each account is represented by one node. One user deletes his account. Thus, the company is legally required to remove all private information of that user. This includes the node associated with the user’s account and the vector representation of that node that is generated by the embedding. The company, however, does likely not delete the vector representations of the other nodes even though the removed node was used during training of these. Is it possible to identify the neighbors of the removed node? Which kinds of neighbors can be identified best, which cannot be identified? First results suggest that the identification of neighbors works well for some kind of nodes and is more difficult for others.
Bachelor
Systematic Literature Reviews - Pushing the Limits
A systematic literature review is a "systematic, explicit, comprehensive, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners".
Master
Graph-Structured Query Construction for Natural Language Questions
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.
Master
Gamification of Serious Games with Recommendation Support
Supervised by PD Dr. Ralf Klamma, AOR, Andreas Herrler; Advisor(s): Benedikt Hensen, M.Sc.
The goal of this master thesis is to re-design and extend an existing serious games platform as microservices for a gamification framework already realized as microservice architecture.
Master
Machine Economy for Dynamic Configurations of Production Processes
Supervised by Prof. Dr. Thomas Rose; Advisor(s): Thomas Osterland
In the context of Industry 4.0 and the Internet of Things, the autonomization of cyber physical systems is increasingly coming to the fore. Just imagine an autonomous vehicle that offers commuter services against payment. The electric vehicle has to pay tolls, requires energy from charging stations and employs washing services from time to time. Considering all entities in this process as agents that can interact and carry wallets, one can easily envision a machine-to-machine economy. Technical agents decide what tasks to conduct regarding costs, capabilities and earnings as illustrated by the demonstrators of Smart Replenishment Box and Smart Vehicle Control1.
Bachelor
Gamification of Habit Forming in Distance Education
Recent developments show a rise in popularity of Learning Management Systems in an increasingly digital teaching enviroment. However, students can encounter difficulties with sustained motivation without extrinsic influences like communicating with peers. The application of gamification principles to the digital learning process provides motivating factors for students to aid their learning process. Participation in educational courses is a process often spanning many weeks, so a habit-based approach to learning seems prudent.
Master
Modeling for Street Level Crime Prediction
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez, Cristina Kadar, Raquel Rosés Brüngger
The aim of this master thesis is to build a predictive model of crime at street level for a Swiss city, including a tool implementation for visualizing the data and results.
Bachelor
An Intelligent Menu Placement System for Mixed Reality Applications
A common problem with spatial immersive applications is the placement of UI elements in the space. On the one hand, a menu needs to be accessible and on the other hand, it must not be in the way of the user when it is not needed. Moreover, the menus need to adapt to the room’s characteristics, e.g. to avoid problems where menus clip through walls and become unreachable. For mixed reality applications, the environment can vary from small rooms to large lecture halls. UI systems must account for this. For instance, UI elements cannot be placed in a fixed position in space without respecting the spatial mapping of the real environment. Instead, a more intelligent solution is required for positioning UI elements in space.
Bachelor
A Social Bot Success Model
Social Bots (software robots) are computer algorithms that automatically produce content and interact with humans on social media. The goal of this study is to discover success factors that make the use of a (chat)bot successful.
Bachelor
Model-Based View Extraction With GraphQL
Supervised by Prof. Dr. Matthias Jarke, Prof. Dr. Bernhard Rumpe; Advisor(s): Dr. István Koren, Manuela Dalibor
Bachelor
Systematic Evaluation Procedures of Mixed Reality Learning Applications
Mixed reality provides many opportunities for learning applications in formal and informal education. In previous projects, we created different learning applications for various use cases. The applications target technologies like the Microsoft HoloLens, HTC Vive, Oculus Quest 2 and smartphones. Initial pilot evaluations of these applications have already been conducted. However, we would like to have a more detailed and systematic analysis of the application’s characteristics in their intended use cases.
Bachelor
Dynamic Embeddings of Evolving Knowledge Graphs
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.
Bachelor
Algorithmic Approaches to Overlapping Community Detection - Spectral Clustering
Supervised by PD Dr. Ralf Klamma, AOR, Michael Schaub
(Social) Network Analysis is investigating (social) structures of real-world networks. Networks are composed of nodes and links. Communities are sub-networks whose nodes have more links to nodes within the sub-network than to nodes outside the sub-network. Overlapping Community Detection is the problem of identifying nodes in networks that belong to more than one sub-network. The overlapping community detection problem has an enormous importance for different fields of science like biology, neurology, sociology, media science, politics, economics, and computer science. A vast number of papers has been written about various aspects of overlapping community detection like measures of quality of overlapping communities, modeling, visualization. A huge number of algorithms have been proposed. Recently, methods of machine learning and quantum computing have been applied on the problem. This bachelor thesis should research recent algorithmic approaches to community detection algorithms. Results should be integrated in the existing award winning WebOCD framework, a collection of Java-based microservices deployed in a peer-to-peer network. The WebOCD framework will be enhanced with DevOps tools for better deployability. Interests in desk research, formal modeling, and Web programming are prerequisites for this bachelor thesis.
Master
Community Information Systems - A Social Capital Perspective
Supervised by PD Dr. Ralf Klamma, AOR
In this thesis we research the use of social capital in information systems in organizational and non-organizational contexts with the aim to conceptualize framework for community information systems.
Master
Blockchain-based Swarm Learning Framework in Personal Health Train
Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient, and empower citizens to participate in the decision-making process regarding their own health and well-being. With the world-widely emergence of data protection legislation, e.g., General Data Protection Regulation (GDPR) in the European Union, society is more aware of privacy. The sensitive nature of health data prohibits healthcare organizations from collecting and sharing the data. Traditional data analytics methods based on data centralization become less feasible. Distributed approaches shifting algorithms instead of data are solutions to comply with privacy protection regulations.
Bachelor, Master
Performing Distributed Analytics on Decentralised Geo- and Hydrological Data
Supervised by Prof. Dr. Stefan Decker, Prof. Dr. Holger Schüttrumpf; Advisor(s): Sascha Martin Welten, M.Sc., Lennart Schelter, Julian Hofmann
In times of increasing weather extremes, weather forecasts and early warning systems for extreme weathers have gained great promise to prevent property damage or to mitigate the risk to the civilian population. Current approaches are based on different data types, such as geo basic data, weather forecasts or other hydrological data, in several data sources (Salas et al., 2020; Kreklow et al., 2020; Hofmann and Schüttrumpf, 2019) However, these approaches rely on data centralisation, which can pose several challenges. First, the centralisation process itself could act as a bottleneck since vast amounts of spatiotemporal data from multiple data sources are transmitted to one single location. Second, the data has to be harmonised locally such that lightweight data analysis tasks or even data model training can be conducted. Lastly, depending on the data type, well-known data privacy regulations hinder institutions to share their data. One solution to circumvent these immanent problems of data centralisation could be Distributed Analytics (DA) approaches. DA poses a paradigm shift to address the mentioned challenges by bringing the analysis algorithm to the data instead of vice versa. By design, data stays within institutional borders and the data owner keeps the sovereignty over the data.
Bachelor
Machine Learning for Anonymization of Unstructured Text
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
This thesis addresses the problem of identifying personal information in unstructured text using supervised Machine Learning (ML). The final application should be able to recognize and annotate the tokens that make up personal data in an English input text as accurately as possible. First, supervised learning methods, suitable for the task, will be identified. Then, models based on the most promising approaches will be designed and implemented. For comparison, suitable evaluation metrics have to be determined. Finally, the approaches are compared and evaluated against a baseline and each other.
Bachelor
Developing Data Quality Metrics for Power System Modeling
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.
Master
Patterns for Integrating Rule Based and Process Based Model Components of Computerized Clinical Guidelines
Supervised by Prof. Dr. Stefan Decker, Dr. rer. nat. Cord Spreckelsen; Advisor(s): 692050c6199c8bbfb9be2189e82ff904
Master
Collaborative Immersive Learning Analytics
Our approach is a comprehensive and evolutionary sociotechnological learning analytics and design process leading to a flexible infrastructure where professional communities can co-create their wearable enhanced learning solution.
Bachelor
Data Analysis of Micromobility in the City of Aachen
This thesis focuses on the data analysis of micromobility data in Aachen. The goal is to use data science methods to gain insights from the already gathered data. To this end, a suitable data model should be defined, which then enables the analysis of both station-based, free-floating and hybrid shared mobility modes. On this defined data basis, various analyses should be performed.
Bachelor
Collaborative Educational Escape Rooms for Mixed Reality
Supervised by PD Dr. Ralf Klamma, AOR; Advisor(s): Benedikt Hensen, M.Sc.
Based on our Mixed Reality framework for gamification we want to explore the design and implementation of mixed reality escape rooms in collaborative educational settings.
Bachelor, Master
Web Frontend for exchange of NFT artwork
The aim of this thesis is to build a web frontend to browse, mint and exchange NFT (nonfungible token, standard ERC-721) based transactions.
Bachelor
Citation recommendation bot
Current advances in recommender systems allow for context-specific suggestions of relevant literature based on various parameters. As a contextual help in writing scientific papers, a citation recommender system should aid the student by providing a personalized suggestion of publications to consider for the Related Work section of a publication. Hence, for this project, we want to employ mentoring bots to provide suggestions of relevant scientific publications to students to support their literature research.
Bachelor, Master
A Cognitive Modeling-Based Architecture for Realistic Agents in Mixed Reality
Courses in higher education, e.g. at universities, face a challenge regarding scalability. Ideally, every student should be able to contact a mentor to benefit from the mentor’s experience and to reach the learning goals. However, if the number of participants in a course rises, the limited resources of the institution are quickly exhausted. This leads to a high workload for academic staff and decreases the mentoring quality since there is less time for mentors to address the individual needs of the students. A solution to this problem is socio-technical support for mentoring processes which combines social processes like peer mentoring and technological processes, e.g. for student’s feedback. As a result, text-based chat bots were created which can answer student’s questions and give feedback about exercises. We would now like to enhance the interaction with such bots by upgrading them to Mixed Reality agents. Such agents are shown as an avatar in a Mixed Reality environment and can interact with virtual content, users and other bots. They form a natural user interface where students can talk to the agent to get advice and it can also make autonomous decisions to guide the learning process. If the agent cannot answer a question, it can call a human mentor to join the conversation. This system considerably lowers the workload of the mentors while improving the mentoring experience of the students.
Master
Building an Universal Community Success Awareness Gamifier
Supervised by PD Dr. Ralf Klamma, AOR; Advisor(s): Dr. István Koren
Every community application can be gamitfied.
Bachelor, Master
Text Mining Techniques for Student Assessments
The goal of this thesis is to integrate the open-source framework ReaderBench into the Kubernetes cluster of the tech4comp project. The thesis should investigate related work in the domain of text mining, natural language processing and educational data mining. The developed extension should integrate into the existing project's infrastructure and provide mentees and their mentors/tutors with advanced feedback on the mentee's reading comprehension.
Bachelor, Master
Social Recommender Systems for Professional Communities
Supervised by PD Dr. Ralf Klamma, AOR
This thesis aims to develop social recommender system service for Open Source Software (OSS) Communities.
Bachelor
A Mixed Reality-Based Card Game for Formal and Informal Education
A challenge of existing educational Mixed Reality applications concerns the difficult accessibility of technology for students. Many applications require expensive head-mounted displays or high-end smartphones which can only be tried by students at the university for a limited amount of time. However, Mixed Reality content can also be shown on a broader range of devices using marker-based technology. Here, a marker is used as an anchor to show a 3D model above it. If the marker is filmed by a smartphone, it can calculate the view angle and render the 3D model from the same perspective. A challenge of marker-based applications is to find a meaningful integration of the markers into the environment. The markers should be easily recognizable but also need to give the user an idea about the 3D model that they can show.