Predicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice. Hardly any attempt has been made to predict the outcome of German cases, although prior court decisions become more and more important in various legal domains of Germany's jurisdiction, e.g., tax law. This paper summarizes our research on training a machine learning classifier to determine likelihood ratios and thus predict the outcome of a restricted set of cases from Germany's jurisdiction. Based on a data set of German tax law cases (44 285 documents from 1945 to 2016) we selected those cases which belong to an appeal decision (5 990 documents). We used the provided meta-data and natural language processing to extract 11 relevant features and trained a Naive Bayes classifier to predict whether an appeal is going to be successful or not. The evaluation (10-fold cross validation) on the data set has shown a performance regarding F 1-score between 0.53 and 0.58. This score indicates that there is room for improvement. We expect that the high relevancy for legal practice, the availability of data, and advance machine learning techniques will foster more research in this area.

In the era of digitalization, IT landscapes keep growing along with complexity and dependencies. This amplifies the need to determine the current elements of an IT landscape for the management and planning of IT landscapes as well as for failure analysis. The field of enterprise architecture documentation sought for more than a decade for solutions to minimize the manual effort to build enterprise architecture models or automation. We summarize the approaches presented in the last decade in a literature survey. Moreover, we present a novel, machine-learning based approach to detect and to identify applications in an IT landscape.

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Abstract. Data-accountability encompasses responsibility for data and the traceability of data flows. This is becoming increasingly important for Socio-Technical Systems (STS). Determining root causes for unwanted events after their occurrence is often not possible, e.g. because of missing logs. A better traceability of root causes can be supported by the integration of accountability mechanisms at design time. We contribute a structured method for designing an accountability architecture for STS at design time. Therefore, we propose the elicitation of accountability goals to answer why an unwanted event happened and who is responsible for it. We also identify four di↵erent interaction types in STS. Additionally, we derive accountability graphs from a generic accountability model for STS that serve as a baseline for designing accountability mechanisms for all relevant entities in an STS. The resulting architecture is adjusted to legal requirements, regulations and contracts. We demonstrate the applicability of our approach with an eHealth case study.