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The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems. [ABSTRACT FROM AUTHOR]

Driven by the booming global business, organisations are required to align their business processes into an inter-connected network. The sophisticated nature of collaboration results in dynamic and complex interactions and correlations between participating business processes. This inevitably poses challenges to business process management in terms of recognising the instance correspondence and analysing the interaction behaviours of collaborative business processes. Nevertheless, this issue has received very limited attention from inter-organisational workflow research. In this paper, a novel correspondence Petri net model called CorPN is developed to specify instance correspondence with extensions to classical WF-Nets. In addition, a method is established to analyse the behavioural properties of CorPN nets for the purpose of process verification and examination. [ABSTRACT FROM AUTHOR]