Event logs are invaluable for conducting process mining projects, offering insights into
process improvement and data-driven decision-making. However, data quality issues affect …

From business processes to course management, variability-intensive software systems
(VIS) are now ubiquitous. One can configure these systems' behaviour by activating options …

Process mining encompasses a series of tasks aimed at discovering knowledge about
business processes from event logs underlying information systems deployed in …

Addressing variability proactively during software engineering activities means shifting from
reasoning on individual systems to reasoning on families of systems. Adopting appropriate …

In Machine Learning (ML), the resolution of anomaly detection problems in time series
presents a great diversity of practices as it can correspond to many different contexts. These …

Digital and simulation models support the design and management of complex systems.
However, system modelling is a time-demanding and knowledge-intensive activity …

This chapter introduces this thesis, and to do so is structured as follows: Firstly, Section 1.1
describes the context in which this thesis is framed and motivates why this work is …

In this chapter, we describe the basis of Feature Models (FMs) using graphical as well as
textual representations. We introduce a smartwatch FM that will be used as a working …

Through constraints, declarative process models represent the permitted behaviour
associated with a business process, by limiting the potential correct traces. These models …

Context-Process Mining is a discipline that intersects business process management and
data science. Event logs are extracted from information systems and analyzed through …