Towards the Detection of Promising Processes by Analysing the Relational Data

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Different traces can be extracted depending on which attribute represents the \(case_{-}id\), what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discovery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This analysis helps to reduce the computational cost of process discovery. For a possible \(case_{-}id\) every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal.