  Process mining aims to comprehend and enhance business processes by analyzing
event logs. Recently, object-centric process mining has gained traction by
considering multiple objects interacting with each other in a process. This
object-centric approach offers advantages over traditional methods by avoiding
dimension reduction issues. However, in contrast to traditional process mining
where a standard event log format was quickly agreed upon with XES providing a
common platform for further research and industry, various object-centric
logging formats have been proposed, each addressing specific challenges such as
object relations or dynamic attribute changes. This makes that interoperability
of object-centric algorithms remains a challenge, hindering reproducibility and
generalizability in research. Additionally, the object-centric process storage
paradigm aligns well with a wide range of object-oriented databases storing
process data.
  This paper introduces a specifications framework from three perspectives
originating from process mining (what should be analyzed), object-centric
process modeling (how it should be modeled), and database storage (how it
should be stored) perspectives in order to compare and evaluate object-centric
log formats. By identifying commonalities and discrepancies among these
formats, the study delves into unresolved issues and proposes potential
solutions. Ultimately, this research contributes to advancing object-centric
process mining by facilitating a deeper understanding of event log formats and
promoting consistency and compatibility across methodologies.
