Module overview¶

Every public name is importable from the top-level ocpm_engine package (from ocpm_engine import StandaloneEngine, dfg_conformance, ...); the two SQL-compilation modules are imported by module path. Algorithms run in the native Rust extension (ocpm_engine._native) with the GIL released, and the Python layer stays a thin typed facade. Current version: 1.1.0.

Each module page carries the full API and function specs with example usages:

Module	Purpose
`ocpm_engine.standalone`	File-based analysis: `StandaloneEngine` over OCEL JSON, XES, SQLite, or DuckDB Parquet, plus `serialize_model`
`ocpm_engine.engine`	PostgreSQL compatibility API: `OcpmEngine` query planning and execution over pg_ocpm
`ocpm_engine.analytics`	Conformance, prediction, bottleneck, and drift scoring from compact aggregate rows
`ocpm_engine.event_batches`	Native summarization of factorized pg_ocpm event batches
`ocpm_engine.bindings`	Native decoding of compact binding-result capsules
`ocpm_engine.models`	Request contracts, query plans, executions, and capability reports
`ocpm_engine.queries`	Parameterized SQL for process-mining response shapes
`ocpm_engine.dynamic_queries`	Compilation of composable dynamic filters into exact DFG queries

A typical standalone flow touches one module; a typical PostgreSQL flow composes four:

from ocpm_engine import (
    EventLogRequest,     # models: request contract
    EventLogWindow,
    OcpmEngine,          # engine: planning and execution
    dfg_conformance,     # analytics: scoring
    TransitionCount,
)

engine = OcpmEngine(dataset_id=42, tenant_id=7)
capabilities = engine.inspect_pg_ocpm(cursor)

request = EventLogRequest(
    object_type="Order",
    windows=(
        EventLogWindow(training_start, training_end),
        EventLogWindow(test_start, test_end),
    ),
)
execution = engine.execute_event_log_summary(
    cursor, request, capabilities=capabilities
)
training, test = execution.summaries   # event_batches: EventLogSummary

test_index = {(e.source, e.target): e.frequency for e in test.dfg}
rows = [
    TransitionCount(e.source, e.target, "directly_follows",
                    e.frequency, test_index.get((e.source, e.target), 0))
    for e in training.dfg
]
score = dfg_conformance(rows, coverage=0.95)