Controlled experiments in Process Mining primarily rely on synthetic event logs generated from declarative or procedural process modeling languages, which often lack the flexibility needed for precise experimental setups. In this paper, we introduce a novel log generator designed to address this gap by enabling fine-tuned customization of synthetic logs. The log generator, implemented using the declarative language Answer Set Programming (ASP), allows researchers to define sophisticated scenarios that are impossible to express with standard log generators to create specific experimental conditions. • Fine-grained and flexible event log generation with a new position-based modeling language ( PosLan ) and Answer-Set Programming. • Support for synthetic logs with characteristics that mimic complex real-world patterns. • Variability control of the generated event log via Answer-Set Programming.

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a “stranger” behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is “optimal” according to user-defined goals. Our approach, namelyNegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution.

The Resource Description Framework (RDF [Hayes, 2004]) is a W3C standard language for representing information about resources in the World Wide Web; RDF provides a common framework for expressing this information so it can be exchanged between applications without loss of meaning. In this tutorial, RDF will be presented as a data model in the database sense. Its motivations will be analysed, and its current formal status revised. The data model can be understood both from a graph theoretical perspective and from a logical perspective. While the former has been the focus of most theoretical (see, e.g., [Gutierrez et al., 2004]) and practical approaches to RDF, the logical view of RDF has been mostly neglected by the community so far. Two provably correct (w.r.t. the normative W3C definitions of RDF [Hayes, 2004]) logical reconstructions of RDF will be presented, by reducing (a fragment of) it to a classical first-order framework suitable for knowledge representation (first developed in [de Bruijn et al., 2005]), and by encoding the full RDF data model in the HiLog logic introduced by Kifer et al. several years ago [Chen et al., 1993]. An emphasis will be given to three main characteristics of RDF: the presence of anonymous bnodes, the non-well-foundedness of the basic rdf:type relation, and the presence of the RDF vocabulary in the mode itself.In the second part of the tutorial, the relation of the logical reconstructions of RDF with a database perspective will be introduced. An RDF database is seen as a model of a suitable theory in first order logic or in HiLog. While in the pure RDF sense the two approaches are equivalent, it will be shown how the difference becomes relevant whenever additional constraints (e.g., in the form of ontologies or database dependencies) are introduced in the framework. In order to allow for additional constraints (e.g., in the standard W3C OWL-DL ontology language [Patel-Schneider et al., 2004]) while keeping the framework first order, only a fragment of RDF can be considered; this restriction is not needed if the framework is in HiLog (see, e.g., [Motik, 2005]). Various complexity and decidability results will be summarised. In the last part of the tutorial, the W3C standard query language for RDF (SPARQL [Prud'hommeaux and Seaborne, 2006]) will be presented. SPARQL is currently a candidate recommendation. The core of SPARQL is a conjunctive query language, with the added complication that the data model includes existential information in the form of bnodes, and that bnodes may be returned by the query. The formal semantics of the core query language will be given. The problem of the canonical representation of the answer set will be introduced, since bnodes introduce a behaviour similar to the null values in SQL. Complexity results for query answering will be given for different cases. Finally, the possible extensions of SPARQL with various classes of constraints will be discussed.