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RWTH Aachen
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Supporting Distributed Analytics Workflows with Monitoring and Transaction Bots

Thesis type
  • Bachelor
  • Master
Status Open
Supervisor(s)
Advisor(s)

In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated.

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. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data.

The Personal Health Train (PHT) [1] is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remains in its original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles.

 

Nevertheless, due to its highly distributed nature, PHT ecosystems suffer from non-transparent activities and their monitoring.

One solution to improve the usability of such an architecture might be the integration of (social) bots. The Social Bot Framework [2] allows a model-driven construction and utilization of social bots. Here, end users can fall back on well-documented REST APIs and thus access external services. The underlying architecture uses an agent-based system, which ensures that the exchanged data is always under the control of the end users.

The focus of this thesis is the conceptualization, implementation, and evaluation of HEIKO, a monitoring and transaction bot, which assists researchers and users of the Distributed Analytics infrastructure in their algorithm development, model training, and its monitoring. Therefore, it is important to present the information in a concise and user-friendly way by means of a chat interface (e.g. Slack).

Possible applications might be the tracking of the current algorithm location, the current status or the retrieval of the calculated results. 
Finally, the developed bot should be compared with conventional monitoring solutions.

If you are interested in this thesis, a related topic or have additional questions, please do not hesitate to send a message to welten@dbis.rwth-aachen.de or neumann@dbis.rwth-aachen.de

References:

[1] Oya Beyan, Ananya Choudhury, Johan van Soest, Oliver Kohlbacher, Lukas Zimmermann, Holger Stenzhorn, Md. Rezaul Karim, Michel Dumontier, Stefan Decker, Luiz Olavo Bonino da Silva Santos, Andre Dekker; Distributed Analytics on Sensitive Medical Data: The Personal Health Train. Data Intelligence 1 January 2020; 2 (1-2): 96–107. doi: https://doi.org/10.1162/dint_a_00032
[2] A. T. Neumann, P. de Lange and R. Klamma, "Collaborative Creation and Training of Social Bots in Learning Communities," 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC), Los Angeles, CA, USA, 2019, pp. 11-19, doi: 10.1109/CIC48465.2019.00011.

Prerequisites

RESTApi Development
(Social) Bots

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