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An exponential growth of event data can be witnessed across all industries. Devices connected to the internet (internet of things), social interaction, mobile computing, and cloud computing provide new sources of event data and this trend will continue. The omnipresence of large amounts of event data is an important enabler for process mining. Process mining techniques can be used to discover, monitor and improve real processes by extracting knowledge from observed behavior. However, unprecedented volumes of event data also provide new challenges and often state-of-the-art process mining techniques cannot cope. This paper focuses on “conformance checking in the large” and presents a novel decomposition technique that partitions larger process models and event logs into smaller parts that can be analyzed independently. The so-called Single-Entry Single-Exit (SESE) decomposition not only helps to …