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  • Hansmann, Thomas (1)
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Robustness of centrality measures (2020)
Martin, Christoph
Network analysis methods have long been used in the social sciences. About 25 years ago, these methods gained popularity in various other domains and many real-world phenomena have been modeled using networks. Well-known examples include (online) social networks, economic networks, web graphs, metabolic networks, infrastructure networks, and many more. Technological development made it possible to store and process data on a scale not imaginable decades ago — a development that also includes network data. A particular characteristic of network data is that, unlike standard data, the objects of interest, called nodes, have relationships to (possibly all) other objects in the network. Collecting empirical data is often complicated and cumbersome, hence, the observed data are typically incomplete and might also contain other types of errors. Because of the interdependent structure of network data, these errors have a severe impact on network analysis methods. This cumulative dissertation is about the impact of erroneous network data on centrality measures, which are methods to assess the position of an object, for example a person, with respect to all other objects in a network. Existing studies have shown that even small errors can substantially alter these positions. The impact of errors on centrality measures is typically quantified using a concept called robustness. The articles included in this dissertation contribute to a better understanding of the robustness of centrality measures in several aspects. It is argued why the robustness needs to be estimated and a new method is proposed. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can be used as a basis for decision making. The relationship between network properties and the robustness of centrality measures is analyzed. Experimental and analytical approaches show that centrality measures are often more robust in networks with a larger average degree. The study of the impact of non-random errors on the robustness suggests that centrality measures are often more robust if missing nodes are more likely to belong to the same community compared to missingness completely at random. For the development of imputation procedures based on machine learning techniques, a process for the evaluation of node embedding methods is proposed.
Empirical development and evaluation of a maturity model for big data applications (2016)
Hansmann, Thomas
Since 2000, data generation has been growing rapidly from various sources, such as Internet usage, mobile devices and industrial sensors in manufacturing. As of 2011, these sources were responsible for a 1.4-fold annual data growth. This development influences practice and science equally and led to different notations, one of the most popular one is Big Data. Besides organization with a business model based solely on Big Data, companies have started to implement new technologies, methodologies and processes in order to deal with the influx of data from different sources and structures and benefit the most of it. As the progress of the implementation and the degree of professionalism regarding data analysis differs amongst industries and companies, latter ones are faced with a lack of orientation regarding their own stage of development and existing relevant capabilities in order to deal with the influx of data as only a few best practices exist. Therefore, this research project develops a maturity model for the assessment of companies capabilities in the field of data analysis with a focus on Big Data. Basis for the model development is a construction model, developed along the criteria of Design Science Research. The developed model contains the different levels of maturity and related measurements for the evaluation of a companies Big Data capabilities with a focus on topics along the dimensions data and organization. The developed model has been evaluated based an application to different companies in order to ensure the practical relevance. The structure of the thesis is the following: In a first step, a structured literature review is carried out, focussing on existing maturity models in the field of Big Data and nearby fields as Business Intelligence and Performance Management Systems. Based on the identified white spots, a design science research oriented construction model for the maturity model development is designed. This model is applied subsequently.
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