  Process mining involves discovering, monitoring, and improving real processes
by extracting knowledge from event logs in information systems. Process mining
has become an important topic in recent years, as evidenced by a growing number
of case studies and commercial tools. Current studies in this area assume that
event records are created separately from a conceptual model (CM). Techniques
are then used to discover missing processes and conformance with the CM, as
well as for checks and enhancements. By contrast, in this paper we focus on
modeling events as part of a tight multilevel CM that includes a static
description, dynamics, events-log scheme, and monitoring and control system. If
there is an out-of-model event log, it is treated as a requirement needed to
build or enrich the CM. The motivation for such a unified system is our thesis
that process mining is an essential component of a CM with built-in mining
capabilities to perform self-process mining and attain completeness.
Accordingly, our proposed conceptual model facilitates collecting data
generated about itself. The resultant framework emphasizes an integrated
representation of systems to include process-mining functionalities. Case
studies that start with event logs are recast to evolve around a model-first
approach that is not limited to the initial event log. The result presents a
framework that achieves the aims of process mining in a more comprehensive way
