Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer performance issues with real-life event logs or limit their expressiveness to a specific set of constaint types. Lately, with RelationalXES a relational database architecture for storing event log data has been introduced. In this technical report, we introduce a mining approach that directly works on relational event data by querying the log with conventional SQL. We provide a list of SQL queries for discovering a set of commonly used and mined process constraints.
In an event log, every process instance corresponds to a sequence () of recorded entries, namely, . We require that events contain an explicit reference to the enacted activity. For discovering resource-related aspects we additionally require an explicit reference to the operating resource. Both conditions are commonly contained in real-world event logs @cite_13 .
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The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool destined to play a key role in business process management.