Graph-Driven Temporal Consensus for Distributed Data Sourcing: AI Augmented Conflict Resolution, Relationship Modeling, and Multi-Source  Integrity Using JanusGraph and Apache Flink on Kubernetes

Jyothish Sreedharan

Graph-Driven Temporal Consensus for Distributed Data Sourcing: AI Augmented Conflict Resolution, Relationship Modeling, and Multi-Source Integrity Using JanusGraph and Apache Flink on Kubernetes

Authors

Jyothish Sreedharan

Keywords:

Graph-Based Relationship Modeling, Temporal Consensus Algorithm, Event-Time Processing, Conflict Detection, Data Provenance, Distributed Stream Processing

Abstract

Distributed data systems face major challenges when combining events from multiple independentsources. Each source operates with its own timing and data formats. Events arrive out of order. Fieldvalues conflict. Entity references do not match. These problems hurt data quality in downstream systems. Traditional pipelines use simple solutions like timestamp

References

David L. Mills, "Precision Synchronization of Computer Network Clocks," ACM. [Online].

Available: https://dl.acm.org/doi/pdf/10.1145/185595.185651

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Published

2026-01-16

How to Cite

Jyothish Sreedharan. (2026). Graph-Driven Temporal Consensus for Distributed Data Sourcing: AI Augmented Conflict Resolution, Relationship Modeling, and Multi-Source Integrity Using JanusGraph and Apache Flink on Kubernetes. Journal of Computational Analysis and Applications (JoCAAA), 35(1), 397–406. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4712