Process Mining allows organizations to obtain actual business process models from event
logs (discovery), to compare the event log or the resulting process model in the discovery …

During the last years, a number of studies have experimented with applying process
discovery techniques with the goal of automatically modelling human routines as if they …

Virtually all techniques, developed in the area of process mining, assume the input event
data to be discrete, and, at a relatively high level (ie, close to the business-level). However …

During the last years, a number of studies have experimented with applying process mining
(PM) techniques to smart spaces data. The general goal has been to automatically model …

Process mining uses computational techniques for process-oriented data analysis. The use
of poor quality input data will lead to unreliable analysis outcomes (garbage in-garbage out) …

Process mining is a technique for exploring models based on event sequences, growing in
popularity in the process industry. Process mining algorithms assume that the processed log …

Process mining enables organizations to analyze the data stored in their information
systems and derive insights regarding their business processes. However, raw data needs …

Event logs are often one of the main sources of information to understand the behavior of a
system. While numerous approaches have extracted partial information from event logs, in …

Process mining, as a well-established research area, uses algorithms for process-oriented
data analysis. Similar to other types of data analysis, the existence of quality issues in input …

A key challenge in data mining, machine learning and artificial intelligence concerns data
quality. Process mining is not an exception. A range of data quality problems exists in …