Ontologydriven extraction of event logs from relational databases Process mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many realworld settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the wellestablished ontologybased data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through useroriented log extraction annotations, which can be flexibly used to provide different logoriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.

When process mining meets bioinformatics Process mining techniques can be used to extract nontrivial process related knowledge and thus generate interesting insights from event logs. Similarly, bioinformatics aims at increasing the understanding of biological processes through the analysis of information associated with biological molecules. Techniques developed in both disciplines can benefit from one another, e.g., sequence analysis is a fundamental aspect in both process mining and bioinformatics. In this paper, we draw a parallel between bioinformatics and process mining. In particular, we present some initial success stories that demonstrate that the emerging process mining discipline can benefit from techniques developed for bioinformatics. Keywords: sequence, trace, execution patterns, diagnostics, conformance, alignment, configuration