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Offene Masterarbeiten

Auf dieser Webseite befindet sich eine Übersicht der aktuell von uns ausgeschriebenen Abschlussarbeiten. Sollte sich unter den Themen kein passendes befinden, dann guck einfach mal in unseren Forschungsthemen nach einem interessanten Bereich und wende dich an die Kontaktperson. Bitte schreib in deiner Anfrage auch gleich etwas über deine aktuellen Vorkenntnisse in dem Bereich.

Development and Evaluation of Augmented / Virtual Reality Applications

Motivation

Cognitive assistance systems offer a variety of possible use cases especially in the context of learning environments in artificial reality (AR/VR) scenarios. In our project it‘s OWL AWARE, we are researching new application scenarios, development methods and new forms of interaction to support the development of novel assistance systems.

In this subject area there are various open Bachelor and Master thesis topics, which deal with the systematic development and evaluation of AR/VR applications.

Possible Topics

  • Systematic literature survey for AR/VR development methods
  • Prototypical development of AR/VR applications for different scenarios (assembly, maintenance, navigation, …)
  • Development of new modeling languages to capture AR/VR specific aspects
  • Development of model-driven or component-based development methods for AR/VR applications
  • Usability Evaluation of AR/VR applications in terms of efficiency, effectiveness, and user satisfaction
  • Data-driven UX Evaluation of AR/VR applications through user feedback (human-in-the-loop, digital-twin approaches)
  • Conception and development of context-adaptive AR/VR applications
  • Continuous quality assurance and software testing for AR/VR applications

If you are interested in one of the above mentioned topics or have your own topic suggestions, feel free to contact us!

Contact

Enes Yigitbas

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Conception and Implementation of a Development Framework for Cross-Device AR/VR Applications

Motivation

Augmented Reality (AR) and Virtual Reality (VR) Developers face a proliferation of new platforms, devices, and frameworks. This often leads to applications and interactions being limited to a single platform and makes it hard to support collaborative AR/VR scenarios involving multiple different devices such as HTC Vive, HoloLens and smartphones. For supporting a seamless interaction across various AR/VR platforms, the main goal of this thesis is to design and implement a framework for cross-device AR/VR experience. Based on previous work in this area [1] relevant cross-device interaction scenarios for AR/VR should be identified. The derived requirements should form the basis to create a development framework for cross-device AR/VR applications. An evaluation of the framework should be performed by implementing exemplary cross-device AR/VR scenarios.

Description of the task

  • Systematic literature survey on cross-device AR/VR interfaces
  • Conception of a development framework for cross-device AR/VR applications
  • Prototypical implementation of a development framework for cross-device AR/VR applications
  • Evaluation by implementing and experimenting with exemplary cross-device AR/VR scenarios

Contact

Enes Yigitbas

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Continuous Adaptation of Business Models based on Feature Models

Motivation

The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs. To represent business models for idea generation, the Business Model Canvas [1] is mostly used. In the past, we have used the concept of feature models to represent the variability of a business model [2]. The goal of this thesis is to conceptualize and implement a tool in Angular, which can (1) mine a feature model out of a set of existing business models, (2) derive new business models from the feature model, (3) learn validated and disapproved business decisions (see Figure 1). To do this standard-techniques like Feature Model Mining [3] or Questionnaire-based Variability Modeling [4] can be applied to the new context of business models.

Tasks

  • Literature Review on Business Models & Feature Models
  • Conception and Design of the Algorithms and the Tool
  • Implementation of the Algorithms and the Tool in Angular
  • Evaluation in a Case Study

Sources

  • [1] Osterwalder et al.: Business Model Generation (2010)
  • [2] Gottschalk et al.: Intertwined Development of Business Model and Product Functions: A Twin Peak Feature Modeling Approach (2019)
  • [3] She: Feature Model Mining (2008)
  • [4] La Rosa et al.: Questionare-based Variability Modeling for System Configuration (2008)

Further Information

The topic can be taken as a bachelor- or master thesis. It is possible to write this thesis in German or English. The requirements for the topic are passed courses of Data Mining (bachelor) or Machine Learning I (master). Please write in your application shortly why you are interested in the topic and what further experience you have in the field. Before the proposal process, there will be a short test (consisting of general questions, your understanding of the topic and a small programming task) to see if you are the right person for the topic.

Contact

Sebastian Gottschalk

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Decision Support System for Business Modeling based on Feature Models

Motivation     

The continuous generation of new business model ideas is an important task for a company to stay competitive. During this process, the company can use various best practice examples of other companies in form pattern cards [1]. To represent business models for idea generation, the Business Model Canvas [2] is mostly used. In the past, we have used the concept of feature models to represent the variability of a business model [3]. Moreover, we have developed a first version of a feature model editor [4] for business models in Angular (see Figure 1). The goal of this thesis is to conceptualize and implement a decision support tool, which can support business developers by automatically applying the various business pattern for improving feature selection during the ideation phase. These patterns can be generalizable to all business models or depending on the context of the business.

Tasks

  • Literature Review on Business Models & Feature Models
  • Conception and Design of the Algorithms and the Tool
  • Implementation of the Algorithms and the Tool in Angular
  • Evaluation in a Case Study

Sources

Further Information

The topic can be taken as a bachelor- or master thesis. It is possible to write this thesis in German or English. The requirements for the topic are passed courses of Data Mining (bachelor) or Machine Learning I (master). Please write in your application shortly why you are interested in the topic and what further experience you have in the field. Before the proposal process, there will be a short test (consisting of general questions, your understanding of the topic and a small programming task) to see if you are the right person for the topic.

Contact

Sebastian Gottschalk

 

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A Systematic Literature Review on Crowdsourcing Problems

Motivation

Crowdsourcing is a process of requesting a large pool of people for solving existing tasks using web-based platforms. We aim to carry out an in-depth study into the crowdsourcing problems in order to categorize and to summarize the current status of the literature on this topic. The research method should follow a systematic literature review approach. This topic is a research-based topic without any need for implementation.

Requirements

  • Good English skills
  • Experience with research
  • Interactive and motivated student
  • Flexible for having two meetings every month

Task description

  • Comparison of different systematic literature review methods
  • Choosing one of the methods with explanation and doing the research according to that
  • Forming a comprehensive categorization of the existing problems together with the detailed description

Contact

Zahra Nouri

 

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A Systematic Literature Review on Data Analysis on Crowdsourcing Platforms

Motivation

Crowdsourcing is a process of requesting a large pool of people for solving existing tasks using web-based platforms. We aim to carry out an in-depth study into the crowdsourcing platforms in order to categorize the existing platforms. Besides, the data analyses, which are conducted on the existing platforms, need to be studied and reported. The research method should follow a systematic literature review approach. This topic is a research-based topic without any need for implementation.

Requirements

  • Good English skills
  • Experience with research
  • Interactive and motivated student
  • Flexible for having two meetings every month

Task description

  • Comparison of different systematic literature review methods
  • Choosing one of the methods with explanation and doing the research according to that
  • Forming a comprehensive categorization of the existing platforms
  • Forming a comprehensive categorization of the data analysis performed on the platforms with all detail description

Contact

Zahra Nouri

 

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A Systematic Literature Review on Task Design Problems in Crowdsourcing

Motivation

Crowdsourcing is a process of requesting a large pool of people for solving existing tasks using web-based platforms. Task design is a complicated job for requesters and they create unclear tasks and so the workers do not provide high quality solutions. Therefore, we aim to carry out an in-depth study into the task design problems in the crowdsourcing processes in order to find where the problems lies and which studies are carried out in this regard. The research method should follow a systematic literature review approach. This topic is a research-based topic without any need for implementation.

Requirements

  • Good English skills
  • Experience with research
  • Interactive and motivated student
  • Flexible for having two meetings every month

Task description

  • Comparison of different systematic literature review methods
  • Choosing one of the methods with explanation and doing the research according to that
  • Forming a comprehensive categorization of the task design problem
  • Forming a comprehensive categorization of the data analyses and solutions to this problem in the literature

Contact

Zahra Nouri

 

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Online Filterung basierend auf der Reisezeit mit verschiedenen Transportmitteln

Motivation

Im Projekt OWL.Kultur-Plattform (https://www.sicp.de/projekte/owlkultur-plattform/) wird unter anderem eine Webseite zur Darstellung der Veranstaltungen im Raum Ostwestfalen-Lippe implementiert. Aktuelle Implementierungen auf anderen Webseiten (wie z. B. Ebay Kleinanzeigen) benutzen die Postleitzahl und Fluglinie, um Angebote zu filtern. Ziel dieser Arbeit ist es einen Filtermechanismus zu implementieren, der eine Filterung nach Reisezeit mit verschiedenen Transportmitteln 
(ÖPNV, Auto, Fahrrad, Fuß) realisiert. Herausforderung hierbei ist die Notwendigkeit, dass von jedem Punkt (Nutzerposition) in kurzer Zeit (ca.  <500ms) ein Ergebnis zur Verfügung gestellt werden muss. Dies ist nicht möglich, wenn man von der Nutzerposition die Reisezeit zu jeder Veranstaltung komplett online berechnet. Daher müssen im Rahmen dieser Arbeit entsprechende Datenstrukturen und Vorberechnungen für die verschiedenen Transportmittel konzipiert und implementiert werden.


Voraussetzung

  • Erfolgreich bestandene Vorlesung Computational Geometry bzw. Algorithms for Highly Complex Virtual Scenes /Algorithmen in der Computergrafik oder ähnliche Veranstaltung
  • Wünschenswert sind Erfahrung in C#, .NET Core, Angular, Typescript

Aufgabenbeschreibung

  • Literaturrecherche bzgl. Reisezeitberechnung
  • Einarbeitung in / Reverse Engineering von Mapnificient für ÖPNV (https://github.com/mapnificent/mapnificent) und/oder Itinero für Fuß / Fahrrad / Auto / ÖPNV / Intermodal (https://www.itinero.tech/) o.ä.
  • Konzept für Vorberechnung und Datenstruktur zur effizienten Filterung
  • Implementierung eines Proof of Concepts
  • Laufzeit-/Performance Analyse (analytisch und/oder empirisch)

Contact

Simon Oberthür

 

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Multi-Variate Online Experimentation for Mobile Applications based on Model-driven Development

Motivation

To improve a software product, many tech companies conduct continuously experiments with their customers. In so-called A/B tests, different versions of a product (e.g. mobile app) are delivered to distinct customer groups (Fagerholm, 2017). From the usage of these different versions, quantitative and qualitative feedback is collected. This feedback is used to improve the product for all customers. One problem of this experimentation is the flexibility of the current A/B testing and the associated problem of parallel experiments. Therefore, the goal of this thesis is to formulate a domain-specific language (DSL), "where engineers and scientists can formulate their problem with [the] flexibility that goes beyond A/B testing“ (Gerostathopoulos, 2019). To solve this problem, different product configurations, experiments, and customer groups have to be represented by the DSL. For this, a DSL for different product configurations, experiments, and customer groups has to be defined and synchronized with the mobile application. Depending on the knowledge of the student, the focus of the thesis can be set (e.g. algorithm development, code generation, coupling of DSL to UI models).

Task

•    Literature Review on Online Experiments
•    Conception and Design of the DSL and synchronization bridge to the mobile application.
•    Prototypical Implementation of the DSL (e.g. Eclipse EMF) and the brigde (e.g. Angular SDK)
•    Evaluation based on a Case Study

Literature

•    Gerostathopoulos, I et al..: Continuous Data-driven
Software Engineering – Towards a Research Agenda (2019)
•    Fagerholm, F., Guinea, A. S., Mäenpää, H., Münch, J.: The RIGHT model for Continuous Experimentation (2017)
•    Fabijan, A., Dmitriev, P., Olsson H.H., Bosch, J.: The Evolution of continuous Experimentation in Software Product Development (2017)
•    Kohavi, R., Longbotham, R.: Online Controlled Experiments and A/B Testing (2017)

 

Contact

Sebastian Gottschalk

 

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Advanced Modeling Techniques for Model-based Product Configuration in AR Applications

Motivation


Augmented reality (AR) has recently found application in mobile-based product configuration, as exemplified by apps like IKEA Place. However, such apps often focus on the visualization of small changes applied to pre-rendered 3D models of the entire product, thus limiting their ability to adapt to the user’s needs and the physical environment at runtime. Furthermore, this approach is unsuitable for products that display high variability in their physical structure. To solve these issues, a previous master thesis introduced a model-based approach to support AR-assisted product configuration. The proposed approach is based on Dynamic Software Product Lines (DSPL) and facilitates dynamic adaptation by applying the DSPL process (Bashari, 2017) to product configuration and (see Fig. 2) enhancing it with domain knowledge stored in feature models (see Fig. 1). While the main concept is already developed and implemented, there are still open research questions regarding the configuration process (e.g. choosing certain colors or maximum price, detecting objects in the environment) and the platform compatibility (e.g. using the same models for AR and VR applications, fluent transfer between different devices). The goal of this master thesis is to overcome some of these limitations by extending the current concept. The exact scope will be developed in the proposal phase of the thesis.


Task

  • Literature Review on DSPLs and AR
  • Conception and Design of the Feature Model Editor and the Application
  • Prototypical Implementation of the Feature Model Editor (based on Angular) and the Application (based on Unity)
  • Evaluation based on a Case Study

Literature

  • Bashari, M., Bagheri, E., Du, W.: Dynamic Software Product Line Engineering: A Reference Framework (2017)

Contact

Sebastian Gottschalk

 

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Masterarbeit: Towards Dealing with API Incompatibility in Android Ecosystem

Contact: Bahar Schwichtenberg

Ausschreibung

Die Universität der Informationsgesellschaft