CompProv Produces Machine Readable Graphs Encoding Microscopic Algebraic Provenance for Reproducible Computation

2026-10-01

AI Summary

This study addresses the limitation of existing provenance systems, which operate at coarse granularity and fail to record algebraic transformation details, rendering computational errors untraceable. To overcome this, we propose a Java-based auditing framework operating at the micro-operation level. By employing high-precision numerical wrappers to capture atomic computations, the method generates self-contained, serializable computational provenance graphs that support input substitution and sensitivity analysis without exposing source code. Experimental evaluations across financial, metrological, and hydrological domains demonstrate that the framework achieves deterministic replay while effectively verifying numerical integrity and temporal auditability. This work establishes a novel paradigm for error tracing in complex computational systems.

Abstract

The reliability of computational results in scientific research and financial modeling increasingly depends on verifiable traceability, not merely on trust in a reported output. Existing provenance systems operate at the granularity of files, datasets, or pipeline stages, leaving the internal sequence of algebraic transformations connecting an algorithm's inputs to its outputs unrecorded; a rounding error, an undocumented substitution, or a missing intermediate value can propagate to a final result with no recoverable trace. This work presents CompProv, a Java-based, audit-oriented provenance framework that captures lineage at the atomic level of individual algebraic operations by encapsulating numerical values in high-precision wrapper objects, producing a serializable Calculation Provenance Graph (CPG) that persists as a self-contained artifact rather than a discarded byproduct. The framework is evaluated through three heterogeneous case studies: a decentralized-finance NAV calculation, a reconstruction of an interferometric gauge-block calibration in metrology under explicitly documented input assumptions, and a hydrological model performance evaluation. Deterministic replay reproduced each result exactly in a fresh environment, and CPG-based input substitution supported sensitivity analysis without exposing the underlying source code. These results indicate that, provided the CompProv runtime and wrapper classes are available in the replay environment, a CPG allows numerical integrity to be audited without disclosing proprietary business logic, and that its self-contained structure supports temporal auditability once an execution environment has become deprecated. This framework establishes an operation-level foundation for auditable-by-design computational systems, with scaling to high-throughput computing identified as a direction for future work.

Problem

• computational provenance
• algebraic traceability
• reproducible computation
• auditability
• lineage tracking

Innovation

• Computational Provenance
• Calculation Provenance Graph
• Deterministic Replay
• Algebraic Operations
• Auditability