Extracting Process-Aware Decision Models from Object-Centric Process
  Data
Alexandre Goossens Johannes De Smedt Jan Vanthienen
Organizations execute decisions within business processes on a daily basis
whilst having to take into account multiple stakeholders who might require
multiple point of views of the same process. Moreover, the complexity of the
information systems running these business processes is generally high as they
are linked to databases storing all the relevant data and aspects of the
processes. Given the presence of multiple objects within an information system
which support the processes in their enactment, decisions are naturally
influenced by both these perspectives, logged in object-centric process logs.
However, the discovery of such decisions from object-centric process logs is
not straightforward as it requires to correctly link the involved objects
whilst considering the sequential constraints that business processes impose as
well as correctly discovering what a decision actually does. This paper
proposes the first object-centric decision-mining algorithm called Integrated
Object-centric Decision Discovery Algorithm (IODDA). IODDA is able to discover
how a decision is structured as well as how a decision is made. Moreover, IODDA
is able to discover which activities and object types are involved in the
decision-making process. Next, IODDA is demonstrated with the first artificial
knowledge-intensive process logs whose log generators are provided to the
research community.