Video Monitoring Multi-Object Correlation Analysis (MOCA)

Increase Operational Efficiencies by Leveraging AI/ML Driven Advanced Analytics and Auto Incidents

Download the IDP Datasheet
Download the MOCA Datasheet

Auto Incidents

Reduce Noise and Enable Live Video Monitoring by Exception

Zixi’s Intelligent Data Platform leverages advanced live video monitoring models to understand KPI and telemetry errors that would signal an issue prior to them happening. MOCA automatically analyzes incidents across all channel sources and target destinations. Incidents are created by grouping low level errors into a meaningful set that share a common attribute, helping operators rapidly identify probable root causes and understand their impacts across seemingly unrelated channels. This enables the operators to dramatically reduce the time it takes to determine the root cause of the problem and significantly improves incident resolution performance.

Correlation Analysis

  • MOCA cuts through the noise of live video monitoring for the quick resolution of issues in minutes instead of days or weeks. 
  • Identifies common cause across channels
  • Greatly speeds up root cause analysis (RCA)
  • Analysis summarizes incident attribution across channel objects, including shared source networks, server networks and/or broadcasters
  • RCA reports with correlation analysis automatically generated complete with corresponding timeframes and time-aligned metric graphs
MOCA analyzes resources that have active incidents and automatically detects correlated attributes, such as a shared server network that is contributing to a high failure rate for channel targets that are using that network.

Consolidated Views of Video Monitoring Results with Interactive Graphs

Incidents are automatically generated and are assigned a name using a default naming convention using the system determined root cause followed by an ASN. Once an incident is selected, all the affected objects are grouped and presented with diagnostic graphs and logs of all the errors occurred over a period. These dynamic graphs help visualize the blast radius while comparing the low-level events simultaneously. Users can select different objects to deep dive into an incident, make notes and share PDF as an RCA document. Incident correlation is performed on dropped packets as well as various ZEN Master errors and warnings including network disconnections, CC errors and low bitrate warnings.

Auto Incidents are accessible within ZEN Master user interface under a separate tab. Once an Incident is selected, all its objects and its respective graphs are displayed helping users better understand the correlation of errors experienced by different objects across a time period indicating a common root cause for those errors. 

Auto Incidents utilize MOCA to determine the root cause of the incident and provide the user with a concise summary of the impacted objects.

New Feature Overview

Zixi Senior Director of Product Management, Andrew Broadstone, provides a detailed overview of the new multi-object correlation analysis available within IDP.

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