@phdthesis{Wolf2017,
  author    = {Wolf, Beat},
  title     = {Reducing the complexity of OMICS data analysis},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-153687},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {The field of genetics faces a lot of challenges and opportunities in both research and diagnostics due to the rise of next generation sequencing (NGS), a technology that allows to sequence DNA increasingly fast and cheap. NGS is not only used to analyze DNA, but also RNA, which is a very similar molecule also present in the cell, in both cases producing large amounts of data. The big amount of data raises both infrastructure and usability problems, as powerful computing infrastructures are required and there are many manual steps in the data analysis which are complicated to execute. Both of those problems limit the use of NGS in the clinic and research, by producing a bottleneck both computationally and in terms of manpower, as for many analyses geneticists lack the required computing skills. Over the course of this thesis we investigated how computer science can help to improve this situation to reduce the complexity of this type of analysis. We looked at how to make the analysis more accessible to increase the number of people that can perform OMICS data analysis (OMICS groups various genomics data-sources). To approach this problem, we developed a graphical NGS data analysis pipeline aimed at a diagnostics environment while still being useful in research in close collaboration with the Human Genetics Department at the University of W{\"u}rzburg. The pipeline has been used in various research papers on covering subjects, including works with direct author participation in genomics, transcriptomics as well as epigenomics. To further validate the graphical pipeline, a user survey was carried out which confirmed that it lowers the complexity of OMICS data analysis. We also studied how the data analysis can be improved in terms of computing infrastructure by improving the performance of certain analysis steps. We did this both in terms of speed improvements on a single computer (with notably variant calling being faster by up to 18 times), as well as with distributed computing to better use an existing infrastructure. The improvements were integrated into the previously described graphical pipeline, which itself also was focused on low resource usage. As a major contribution and to help with future development of parallel and distributed applications, for the usage in genetics or otherwise, we also looked at how to make it easier to develop such applications. Based on the parallel object programming model (POP), we created a Java language extension called POP-Java, which allows for easy and transparent distribution of objects. Through this development, we brought the POP model to the cloud, Hadoop clusters and present a new collaborative distributed computing model called FriendComputing. The advances made in the different domains of this thesis have been published in various works specified in this document.},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{Dombrovski2022,
  author    = {Dombrovski, Veaceslav},
  title     = {Software Framework to Support Operations of Nanosatellite Formations},
  isbn      = {978-3-945459-38-6},
  doi       = {10.25972/OPUS-24931},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-249314},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Since the first CubeSat launch in 2003, the hardware and software complexity of the nanosatellites was continuosly increasing. To keep up with the continuously increasing mission complexity and to retain the primary advantages of a CubeSat mission, a new approach for the overall space and ground software architecture and protocol configuration is elaborated in this work. The aim of this thesis is to propose a uniform software and protocol architecture as a basis for software development, test, simulation and operation of multiple pico-/nanosatellites based on ultra-low power components. In contrast to single-CubeSat missions, current and upcoming nanosatellite formation missions require faster and more straightforward development, pre-flight testing and calibration procedures as well as simultaneous operation of multiple satellites. A dynamic and decentral Compass mission network was established in multiple active CubeSat missions, consisting of uniformly accessible nodes. Compass middleware was elaborated to unify the communication and functional interfaces between all involved mission-related software and hardware components. All systems can access each other via dynamic routes to perform service-based M2M communication. With the proposed model-based communication approach, all states, abilities and functionalities of a system are accessed in a uniform way. The Tiny scripting language was designed to allow dynamic code execution on ultra-low power components as a basis for constraint-based in-orbit scheduler and experiment execution. The implemented Compass Operations front-end enables far-reaching monitoring and control capabilities of all ground and space systems. Its integrated constraint-based operations task scheduler allows the recording of complex satellite operations, which are conducted automatically during the overpasses. The outcome of this thesis became an enabling technology for UWE-3, UWE-4 and NetSat CubeSat missions.},
  subject      = {Kleinsatellit},
  language  = {en}
}