Abstract. The basic idea of process mining is to extract knowledge
from event logs recorded by an information system. Until recently, the
information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Fuelled by the omnipresence of
event logs in transactional information systems (cf. WFM, ERP, CRM,
SCM, and B2B systems), process mining has become a vivid research
area [1, 2]. In this paper we introduce the challenging process mining
domain and discuss a heuristics driven process mining algorithm; the
so-called “HeuristicsMiner” in detail. HeuristicsMiner is a practical applicable mining algorithm that can deal with noise, and can be used to
express the main behavior (i.e. not all details and exceptions) registered
in an event log. In the experimental section of this paper we introduce
benchmark material (12.000 different event logs) and measurements by
which the performance of process mining algorithms can be measured.