Joerg Schoenfisch

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We present a solution for modeling the dependencies of an IT infrastructure and determine the availability of components and services therein using Markov logic networks (MLN). MLNs offer a single representation of probability and first-order logic and are well suited to model dependencies and threats. We identify different kinds of dependency and show how(More)
Handling uncertain knowledge – like information extracted from un-structured text, with some probability of being correct – is crucial for modeling many real world domains. Ontologies and ontology-based data access (OBDA) have proven to be versatile methods to capture this knowledge. Multiple systems for OBDA have been developed and there is theoretical(More)
In this paper we propose an approach for calculating the most probable root cause for an observed failure in an IT infrastructure. Our approach is based on Markov Logic Networks. While Markov Logic supports a special type of deductive inference, known as maximum a posteriori inference, the computation of the most probable cause requires abductive reasoning.(More)
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