Main article

Joris van der Velden
Department of Animal Sciences, Aeres University of Applied Sciences, Dronten 8251 JZ, Netherlands
Anne Bakker-de Vries*
School of Information and Communication Technology, HAN University of Applied Sciences, Arnhem 6826 CC, Netherlands
anne.bakker@han.nl
Pieter Hoogendoorn
Faculty of Engineering, Saxion University of Applied Sciences, Enschede 7513 AB, Netherlands
Sanne Jansen
Research Centre Future of Food, Van Hall Larenstein University of Applied Sciences, Leeuwarden 8934 CJ, Netherlands

DOI: https://doi.org/10.63646/datamind.2025.030203

Abstract

Dairy compliance oversight is increasingly carried out through a combination of on-farm Internet of Things sensors, federated machine learning, and blockchain anchoring, yet the records produced by these stacks are typically scattered across ledger artifacts, model checkpoints, and ad-hoc audit spreadsheets that no single party can query end-to-end. This article reframes the problem as a database design question and presents DairyChainDB, a verifiable compliance database that treats the schema, field dictionary, indexes, quality-control pipeline, and reusable interfaces as the principal contribution. Six core entities (FARM, ANIMAL, COMPLIANCE_PROOF, MODEL_VERSION, FL_ROUND, AUDIT_EVENT) are organized so that every regulatory decision traces back to a verifiable evidence chain that links the underlying federated model version, the cryptographic compliance proof, the audit event, and the responsible farm identifier. The database is organized as a polyglot store comprising a Parquet-plus-Delta lakehouse for raw measurement streams, a PostgreSQL relational core for transactional records, a Neo4j property graph for animal-to-cooperative relationships, a pgvector index for embedding-based similarity search over compliance fingerprints, and an anchored Layer-2 zero-knowledge rollup that records succinct proofs of regulatory rule satisfaction. We benchmark the database on a working subset of 412 simulated dairy farms and 18,640 animals over a six-month observation window, and we report a runnable experiment that raises proof-ingest throughput from 5,860 to 9,820 proofs per second on a 16-node cluster, sustains audit query latency below 463 milliseconds at the 95th percentile, reduces audit case-review time from 62.4 to 8.6 minutes, and holds on-chain verification cost constant at 0.42 US dollars per aggregated proof regardless of farm count. The schema, dictionaries, smart-contract interfaces, and reproduction notebooks are released under an open licence.

Article details

How to Cite

Velden, J. van der, Vries, A. B.- de, Hoogendoorn, P., & Jansen, S. . (2025). A Verifiable Dairy IoT Compliance Database for Privacy-Preserving Livestock Analytics and Federated Model Governance. DATAMIND, 3(2), 20-32. https://doi.org/10.63646/datamind.2025.030203