Concept drift in process mining (PM) is a challenge as classical methods assume processes
are in a steady-state, ie, events share the same process version. We conducted a systematic …

Encoding methods are employed across several process mining tasks, including predictive
process monitoring, anomalous case detection, trace clustering, etc. These methods are …

When a company decides to automate its business processes by means of RPA (Robotic
Process Automation), there are two fundamental questions that need to be answered. Firstly …

Encoding methods affect the performance of process mining tasks but little work in the
literature focused on quantifying their impact. In this paper, we compare 10 different …

A wide range of applications based on sequential data, named time series, have become
increasingly popular in recent years, mainly those based on the Internet of Things (IoT) …

Assuring anomaly-free business process executions is a key challenge for many
organizations. Traditional techniques address this challenge using prior knowledge about …

Process mining provides techniques to learn models from event data. These models can be
descriptive (eg, Petri nets) or predictive (eg, neural networks). The learned models offer …

Trace clustering has been extensively used to discover aspects of the data from event logs.
Process Mining techniques guide the identification of sub-logs by grouping traces with …

Within the realm of business context, process duration signifies time spent by customers
between successive activities. This temporal perspective offers important insight to customer …

In this article we introduce (event STream Analysis for pRocess Discovery Using Sampling
sTragies), a process discovery approach that analyses a trace stream, in order to discover a …