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Information Systems
Prof. Dr. M. Jarke
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Prof. Dr. M. Jarke
RWTH Aachen
Informatik 5
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D-52056 Aachen
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Theses

Information about Diploma/Master thesis process

Open Theses  


Master
A Platform for Creating Social Bots
Posted on 26. Jul 2016; Supervised by PD Dr. Ralf Klamma, AOR
Social Bots (software robots) are computer algorithms that automatically produce content and interact with humans on social media.
Bachelor, Master
Accelerating Graph Embedding using GPUs and Distributed Computing
Posted on 29. Sep 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez, Rezaul Karim
Lately several methods for embedding graphs nodes into a vector space have been proposed. These embeddings can then used to train other machine learning models. Learning these embeddings is typically done using CPUs. In this thesis the student would look into the use of other hardware, like GPUs and distributed computation options to speed up the learning process. The challenge is that algorithms working on graphs have typically a bad memory locality. Hence, existing algorithms might need profound modification in order to use them on GPUs or in a distributed fashion.
Bachelor
An Editor for Prototype-based Knowledge Bases
Posted on 24. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Prototype ontologies are a new approach for knowledge representation. The task of the student is to create an editor for prototype based ontologies, based on the prototype knowledge base code provided. The editor must be intuitive to use and give suggestions to the user. Further, it must show how final values of the prototypes have been derived.
Bachelor
Automatic for the People - Open Badge based Learning Assessment
Posted on 10. Jun 2015; Supervised by PD Dr. Ralf Klamma, AOR
Assigning automatically badges that reflect the learning progress of self-directed learners.
Master
Blockchain
Posted on 21. Jun 2017; 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.
Bachelor
Comparing Communities and Topics in Wikipedias
Posted on 11. Sep 2017; Supervised by PD Dr. Ralf Klamma, AOR; Advisor(s): Bernhard Göschlberger, MLBT MSc BSc, Mohsen Shahriari
Investigate the relationship between communities and topics by applying overlapping community detection to the social network of contributors and subsets of intrawiki link networks on different Wikipedias.
Bachelor, Master
Conversion from RDF to Prototype-based Knowledge Base
Posted on 24. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Prototypes have been proposed recently as a new way to represent knowledge. In recent years many datasets have been published using RDF. Your task is to find out how the RDF dataset can be converted to prototypes in an efficient manner. For a master thesis, you also have to work on the optimal conversion for different requirements such as updates in the original RDF data.
Bachelor
Developing a Data Annotation Tool for Scientific Data Management
Posted on 25. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Semantic technologies and RDF data representation can improve the reusability of scientific data and enable scientist to reproduce the experiments. However there is no tool to support researcher for making their data semantically interpretable by computers. Aim of the thesis is to understand the benefits of semantic web technologies for reproducible research and develop a tool which can convert experimental data to RDF by annotating with selected data models.
Bachelor
Evaluation of Approximate Hierarchical Clustering Algorithms
Posted on 29. May 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
There are several algorithms to perform a hierarchical clustering, resulting in approximate dendrogram. This makes it possible to perform a clustering on big data sets. In this thesis the student will evaluate of several existing algorithms in terms of resource use and clustering quality. As part of this work, the student has to implement some of the algorithms to work on a GPU as they are not scalable enough for CPU computing.
Bachelor
Evaluation of Stream Sampling Algorithms
Posted on 08. Feb 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
The student will implement several stream sampling algorithms and perform experiments to compare their performance. The implementations are done on top of streaming frameworks like Spark, Apache Flink, and Storm.
Master
Immutability for Prototype-based Knowledge Bases
Posted on 24. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Prototypes have been proposed recently as a new way to represent knowledge. Typically, one allow any kind of changes in a dataset. However, when a dataset is distributed with potentially malicious parties, it would be beneficial to make the dataset immutable and use some form of signature to prove authenticity. Moreover, making the data immutable has beneficial properties for caching. In the immutable scenario, the only way to make changes is by adding more prototypes. The student's task is to investigate the different options to make this immutable prototype store happen. This requires studying things like block chains and the internal git storage model. Further, the thesis can include some mechanisms to simulate some sort of mutability (e.g. by combining immutable and mutable parts).
Bachelor, Master
Including Attributes in a Graph Embedding
Posted on 29. Sep 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Lately several methods for embedding graphs nodes into a vector space have been proposed. These embeddings can then used to train other machine learning models. Most approaches will, however, only keep relations between nodes representing entities in the graph into account. If the graph also has nodes representing literal values (numbers, strings, etc.) then they are ignored. In this thesis, the student will investigate how these attributes can be included in the embedding.
Master
Interactive Support for Business Modelling
Posted on 21. Jun 2017; Supervised by Prof. Dr. Thomas Rose; Advisor(s): Thomas Osterland
A business model is an abstract model of the business of one or more cooperating organisations. It is a conceptual and architectural implementation of a business strategy and the foundation for the implementation of business processes and information systems. The research objective of this thesis is the design, implementation and evaluation of an interactive tool for the engineering of a business model.
Master
It's the Media, Stupid: Identifying Media-Specific and Time-Dependent Patterns in Community Success Models
Posted on 31. May 2016; Supervised by PD Dr. Ralf Klamma, AOR
Assuming that best practices for organizing work and learning emerge from the visual analytics and comparison of real communities, the goal of this thesis is to analyze media transcription processes from real communities using data mining and machine learning.
Master
Locality-sensitive Hashing using not-so-random Hash Functions
Posted on 24. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Locality-sensitive hashing is used to speed up near-neighbor search in high dimensional space. When the distance of interest is cosine distance, Random hyperplane hashing (RHH) is used. This technique is based on randomly selecting hyperplanes. However, in some cases (when we have more information about the dataset) it seems reasonable to not choose the hyperplanes completely randomly. Further, if normal RRH is performed with a low number of hyperplanes, then the hyperplanes are likely to not cover the space very well. This thesis will be about choosing the hyperplane in a data dependent way and try to sample the hyperplanes such that they cover the space nicely (including a comparison with angular quantization).
Master
Locality-sensitive Hashing with Undecisive Hash Functions
Posted on 24. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
Locality-sensitive hashing (LSH) is used to speed up near-neighbor search in high dimensional space. LSH works by hashing the elements to discrete buckets. However, in some cases the hash function has to make a decision which leads to similar points being hashed apart. This, for instance, happens when a point is close to a hyperplane in RHH. One solution to this problem is to hash several small perturbations of the points and insert all of them into the indexes. Other solutions also exist. This thesis will look into the different options for improving the performance of LSH by hashing points to multiple buckets instead of just one.
Master
Measuring coherence accross media in learning environments
Posted on 28. Sep 2017; Supervised by PD Dr. Ralf Klamma, AOR, Dr. Marc Spaniol
Computer linguistics has provided impressive results for measuring the quality of writing, e.g. for automatic essay scoring. Multimedia content based indexing delivered a lot of models for the analysis of multimedia materials. In modern educational platforms, e.g. MOOCs and self-regulated learning platforms. In consequence, multimedia materials are produced by educational designers but also by the learners during their learning processes. Coherence is a semantic measure for the local and global connectivity of e.g. sentences, paragraphs, videos, slides among others. To measure the coherence of multimedia materials many computational methods reaching from natural language processing to machine learning needs to be combined in a common coherence model. Goal of this master thesis is to co-develop a coherence model for cross-media coherence and to prototypical combine a few of these computational methods for one or two analysis scenarios, e.g. a MOOC or a webinar.
Bachelor, Master
Optimizing Mining Maximal Frequent Patterns with MFPAS
Posted on 29. Sep 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez, Rezaul Karim
Recently, a new approach for finding maximal frequent patterns (MFPAS) was presented by the supervisor and advisers of this thesis. Several further optimizations of the algorithm are possible. The student working on this thesis will experiment with different optimization possibilities and analyse their effect experimentally. For a master thesis, further theoretical analysis of the optimizations and the original algorithm are necessary.
Master
Post-Mortem Community Information Systems Success Analytics
Posted on 31. May 2016; Supervised by PD Dr. Ralf Klamma, AOR
Goal of this thesis is an integration of post-mortem community data dumps with the MobSOS real-time community information systems success awareness framework.
Bachelor, Master
Secure Evaluation of Knowledge Graph Merging Gain
Posted on 08. Aug 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez, Benjamin Heitmann, Ph.D., Naila Karim
Alice wants to sell a knowledge graph (KG) to Bob, who wants to use it to extend his own KG. Bob wants to know how much it should pay Alice for the data. The only way seems to be that Alice gives her KG to Bob for evaluation. The problem is now that Alice cannot know that Bob is not going to keep the dataset. In this thesis, the student will look into algorithms to securely estimate the gain from combining these two KGs without actually sharing them.
Bachelor, Master
Sensor Fusion for AR Devices in Training
Posted on 31. Aug 2017; Supervised by PD Dr. Ralf Klamma, AOR, Roland Klemke; Advisor(s): Dipl.-Medieninf. István Koren
An immersive Augmented Reality (AR training development framework needs support for combinations of a wide range of appropriate devices for different use cases. In a surgery training situation this can be an AR headset and a handtracking device capturing exact movements in order to evaluate the accuracy. Therefore, it is necessary to get information from different sensors and interpret them in a common sensor fusion framework, in order to record the actions. The goal of the thesis is twofold. First, the WEKIT.one framework will be extended. New sensor hardware, which can be used to improve the training experience of apprentices, will be implemented, tested and evaluated against the given requirements. The implementation is based on the Unity SDK in combination with Microsoft HoloLens and other AR relevant devices. Second, we are interested in visual learning analytics of the gathered data. Therefore, means for storing learner traces both locally and externally will be evaluated with the intention to use the stored data for long-term analytics. Contributions will be specified and implemented with the help of the upcoming IEEE standard on Augmented Reality Learning Experience Models (ARLEM). As a Bachelor thesis, the scope can be adjusted.
Master
Service Provisioning for Mobile Edge Cloud Computing
Posted on 29. Sep 2017; Supervised by PD Dr. Ralf Klamma, AOR
Mobile edge cloud computing provides a platform to accommodate the offloaded traffic workload generated by mobile devices. It can significantly reduce the access delay for mobile application users. However, the high user mobility brings significant challenges to the service provisioning for mobile users, especially for the delay-sensitive mobile applications. We want to research how to update the service provisioning solution for a given community of mobile users. Therefore, we compare current offloading strategies, information structures based on peer-to-peer in combination with cloud computing and client-side solutions using advanced Web protocols like WebRTC. The thesis continues our successful research record in mobile cloud computing,
Master
Travel Assistance using Natural Language
Posted on 12. Oct 2017; Supervised by Prof. Dr. Matthias Jarke; Advisor(s): Dipl.-Inform. Christian Samsel, Dr. Karl-Heinz Krempels
Previously, we developed approaches and solutions for travel assistance using e.g. a wearable device, as well as a prototype for a natural language travel information system. We'd like to combine these two approaches into a combined prototype and test it in the field.

Running Theses


Completed Theses


Master
The Pragmatics and Logic of Knowledge Representation with Prototypes
Gesche, Gierse in 2017; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
The goal of this thesis is to explore possible knowledge representation mechanisms for prototypes. These will then be used to introduce basic reasoning and integrity checking on prototypes. Constraints will be introduced as knowledge representation mechanism since they are common to knowledge representation and integrity checking.