  A process discovery algorithm aims to construct a model from data generated
by historical system executions such that the model describes the system well.
Consequently, one desired property of a process discovery algorithm is
rediscoverability, which ensures that the algorithm can construct a model that
is behaviorally equivalent to the original system. A system often
simultaneously executes multiple processes that interact through object
manipulations. This paper presents a framework for developing process discovery
algorithms for constructing models that describe interacting processes based on
typed Jackson Nets that use identifiers to refer to the objects they
manipulate. Typed Jackson Nets enjoy the reconstructability property which
states that the composition of the processes and the interactions of a
decomposed typed Jackson Net yields a model that is bisimilar to the original
system. We exploit this property to demonstrate that if a process discovery
algorithm ensures rediscoverability, the system of interacting processes is
rediscoverable.
