Deterministic method improves identity record reconciliation accuracy
Deterministic Temporal Reconciliation for Effective-Dated Identity Lifecycle Events
Software EngineeringDistributed, Parallel, and Cluster Computing
Summary
When companies update employee records, events can arrive out of order or with corrections, causing confusion about the current truth. The authors introduce a way to sort and combine these changes that guarantees the right final record every time. Their method treats each update carefully by picking the freshest, canceling duplicates, and applying changes in the right sequence. Tests with simulated data showed it outperforms other common approaches, producing perfect accuracy even when updates arrive very out of order.
What this means in practice
- •For identity management teams: Make workforce identity records reliable despite corrections, future changes, and out-of-order data deliveries by applying a deterministic reconciliation method.
- •For financial compliance teams: Ensure audit-ready employee data snapshots by removing conflicting updates and applying authoritative revisions in temporal order.
Authors
Pramod Ubbala
Abstract
Identity lifecycle automation processes workforce changes that carry two distinct notions of time: when a business fact becomes effective and when an authoritative source last revised it. Corrections, cancellations, future-dated changes, duplicate delivery, and out-of-order arrival can therefore produce divergent account states when processors rely on transport order or effective time alone. This paper presents a deterministic temporal reconciliation method for effective-dated identity events. The method separates logical business facts from their physical revisions, deduplicates exact replays, selects the authoritative revision of each fact using transaction-time freshness, removes canceled facts, and then applies surviving facts in valid-time order. We establish permutation invariance, replay idempotence, revision dominance, and deterministic snapshot construction under explicit source assumptions. A reproducible synthetic fault-injection benchmark compares the method with arrival-order, effective-time, and transaction-time processing. Across 250,000 generated identity histories and 1,585,589 delivered records, the proposed method reconstructed the separately retained authoritative final state in all tested cases. At 75% delivery disorder, exact-state accuracy was 42.35% for arrival-order processing, 85.38% for effective-time sorting, 76.61% for transaction-time sorting, and 100% for the proposed method. A separate 40,000-permutation experiment produced the same final state for every tested permutation under the proposed reconciliation contract.