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Informatik 5
Information Systems
Prof. Dr. M. Jarke
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Prof. Dr. M. Jarke
RWTH Aachen
Informatik 5
Ahornstr. 55
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
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.
Master
Application of Deep Learning to Biomedical Data Sets to Discover Patient Similarities
Posted on 25. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Biomedical data repositories provide rich data for genomic and clinical characteristics of patients for diverse diseases. Predicting patient similarities from these data sets can reveal underlying patterns for diseases. Deep learning provides a computational model composed of multiple processing layers to learn with multiple levels of abstraction. These algorithms have been successfully used in many domains including object classification in images. The aim of this thesis is to improve the pattern detection in biomedical data by applying novel deep learning techniques.
Bachelor
Applying Multi-omic Integrative Data Analysis to Discover Similar Patterns in Multiple Diseases
Posted on 25. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Big data analytics helps us to understand the underlying causes for disease comorbidities (co-occurrence of chronical diseases) based on similar molecular, genetic, environmental and lifestyle risk factors. The aim of this study is to apply data mining algorithms to the multi omic and clinical data to identify similar patterns in commonly observed phenotypes. The study will include main steps of biomedical data analytics such as: understanding, transforming and cleaning data sets; feature selection; exploring outcomes of alternative integration strategies; applying previously developed algorithms (such as iCluster, Pogo, comoR) which are already available as an R toolbox; and compare their performance.
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.
Master
Design and implementation of an adaptive community evolution algorithm
Posted on 13. Oct 2016; Supervised by Prof. Dr. Matthias Jarke; Advisor(s): Dr. Zinayida Petrushyna (Kensche), M.Sc.
With the help of community evolution we can find out how customer communities expand or how Facebook users react on public incitement.
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, Master
Developing a decentralised personalisation approach for secure peer to peer environments
Posted on 01. Nov 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Benjamin Heitmann, Ph.D.
Personalisation has become an expected part of smart services. However, the majority of current personalisation approaches have been designed as centralised services with one central store of user profile data.
Bachelor
Developing a Linked Open Data Registry plugin for Taverna Workflows
Posted on 25. Oct 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Analyzing and processing open RDF data sources is vital for stimulating new discoveries in medicine. The Taverna workflow management system is an Apache incubator project ( https://taverna.incubator.apache.org/ ). The system enables scientist to create and store re-executable data analytics workflows which are described in the XML-based language SCUFL. Taverna is adopted by many research groups working in a variety of fields including bioinformatics (such as transcriptomics, proteomics and metabolomics), text mining, biodiversity and the Virtual Physiological Human. For life scientists, it is important to access distributed, changing sources such as Bio2RDF and DisGeNET-RDF to improve their outcomes. In this thesis, a Taverna plug-in will be developed to access open RDF data sources. The plug-in will browse services in RDF endpoints, query metadata, and add them to Taverna workflows.
Bachelor, Master
Developing a personalisation approach using distributed processing on the blockchain with Ethereum
Posted on 01. Nov 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Benjamin Heitmann, Ph.D.
Blockchain-based technologies, such as Bitcoin have seen an influx of developers due to their applicability in the financial sector. However, their applicability goes far beyond financial use cases, as the blockchain provides solutions for several other important problems such as maintaining an arbitrarily complex state between an unlimited number of participants. Several approaches have emerged to allow running of code on the block chain which uses the state of the blockchain to control the code.
Master
Developing of a privacy enhancing personalisation approach which hides user data from cloud service providers
Posted on 01. Nov 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Benjamin Heitmann, Ph.D.
In order to provide scalable and smart user experiences, the backend of the majority of user facing services uses “the cloud”. However, cloud computing introduces new threats and adversaries in the areas of security and privacy as the cloud operator himself could try to gain access to user data.
Bachelor, Master
Developing of benchmarking suite for libraries which enable algorithms to use encrypted user data
Posted on 01. Nov 2016; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Benjamin Heitmann, Ph.D.
In the last decade several approaches have been developed for processing encrypted data without decrypting it. These approaches have the potential to enable a new generation of privacy-enabling technologies which offers the user hard privacy guarantees in the context of data science.
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
Increasing efficiency of the CUDA-based library for community detection
Posted on 13. Oct 2016; Supervised by Prof. Dr. Matthias Jarke; Advisor(s): Dr. Zinayida Petrushyna (Kensche), M.Sc.
The thesis is perfect for those who wants to extend their knowledge in programming on GPUs with CUDA library and write their code proactively resulting in high performance result.
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; Advisor(s): Dr. Dominik Renzel, Dipl.-Inform.
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
Post-Mortem Community Information Systems Success Analytics
Posted on 31. May 2016; Supervised by PD Dr. Ralf Klamma, AOR; Advisor(s): Dr. Dominik Renzel, Dipl.-Inform.
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
The Learning Toolbox
Posted on 20. Feb 2014; Supervised by PD Dr. Ralf Klamma, AOR
The Learning Toolbox is a mobile application supporting apprentice education in German construction industry.

Running Theses


Completed Theses