Spatiotemporal Context Detection In Distributed Systems Events With An Application On Network Communication Alarms

Distributed systems, characterized by their inherent heterogeneity, frequently encounter challenges in managing and interpreting a multitude of events. In this study, we advocate for a standardized methodology designed to address these issues in diverse distributed environments. The proposed approach focuses on identifying spatiotemporal contextual factors and understanding inter-and intra-asset dependencies. Our methodology utilizes the Markov clustering algorithm to enable a systematic exploration of event data across different contexts. We illustrate the versatility of this approach by applying it to the specific domain of telecommunication network alarms. The results demonstrate how the methodology efficiently clusters and manages alarm data within complex distributed systems, offering a valuable tool for general event management in diverse environments.