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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
Tel +49/241/8021501
Fax +49/241/8022321

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Running Theses

Efficient Visualization of High Dimensional Data Sets
Data in high dimension are difficult to visualize and understand. With an increasing growth of such high dimensional data sets it is imperative to look into the current state of the art algorithms and analyze how they handle such challenges.
Bachelor, Master
Sensor Fusion for AR Devices in Training
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 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.
Go with the Flow - Exponential Decaying Reservoir Sampling of Evolving Data Streams
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
This thesis deals with sampling data streams in such a fashion that recent items are more likely to be part of the sample. The main target is to provide a theoretical analysis of the properties of algorithms.
Parking availability prediction using smart sensors and machine learning
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Michael Cochez
In this thesis the student investigates machine learning models for predicting parking space availability. Also the use of the semantic sensor network vocabulary is studied.
Bachelor, Master
Secure Evaluation of Knowledge Graph Merging Gain
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.
Semantic Data Profi ling in Data Lake
Scope of the thesis is to extend current semantic pro ling efforts for data lakes with ontologies enrichment. Develop tools to systematically extract, manage and exploit metadata of the datasets' information and display the updated datasets with semantic or syntactic correct results.
Data Analysis in the Industry Inferring causal relations in Industrial Data
Supervised by Prof. Dr. Matthias Jarke, PD Dr. Christoph Quix; Advisor(s): Dr. Christoph Paulitsch, Dr.-Ing. Matthias Loskyll
A Platform for Creating Social Bots
Supervised by PD Dr. Ralf Klamma, AOR; Advisor(s): Peter de Lange, M.Sc.
Social Bots (software robots) are computer algorithms that automatically produce content and interact with humans on social media.
Smart Quality Check for FHIR Resources System
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Fast Healthcare Interoperability Resources defines a set of "Resources" that represent granular clinical concepts. It is a standard for exchanging healthcare information electronically between stakeholders of the healthcare environment including care providers, patients, and mobile application developers. Aim of this thesis is to develop tools to validate conformance of the resources against a set of business rules.
Analysis of Breast Cancer Genomic Data with Multimodal Deep Belief Network
The objective of this thesis is to analyse breast cancer genomic data with the potential of predicting breast cancer genomic biomarker. Specifically, these analysis will include classification and regression of breast cancer patients based on their genetic information.
Using Deep Neural Networks and Copy Number Variations for Cancer Detection
Recently copy number variations got lot of attention due to their association with complex diseases like cancer. Finding their association with cancer is an active research eld. We propose a method to identify copy number variations which might be associated with a cancer by comparing copy numbers across 14 major cancer types. Selected CNV regions were used as features for cancer detection using deep belief networks, a deep learning technique. To our knowledge this is the rst study involving that many cancers and highest number of control samples for validation.