Event Automation & Correlation Rules

Transform your event management from reactive to proactive with NopeSight's intelligent correlation rules engine. Our platform automatically detects complex patterns, identifies root causes, and provides actionable remediation suggestions to accelerate incident resolution.

Intelligent Rule-Based Correlation

Advanced Pattern Detection

NopeSight's correlation rules engine goes beyond simple time-based grouping to identify complex patterns in your infrastructure:

Database Connection Issues When connection pool exhaustion occurs, the system automatically:

• Correlates connection pool alerts with application timeouts
• Identifies the root cause as database capacity
• Groups all related events within a 5-minute window
• Provides remediation suggestions like increasing pool size or optimizing queries

Cascading Service Failures The platform detects when failures cascade through your infrastructure:

• Tracks dependency relationships between services
• Identifies upstream root causes
• Correlates downstream impacts within 3-minute windows
• Automatically determines business impact severity

Memory Leak Detection Early warning system for gradual resource exhaustion:

• Monitors memory usage trends over time
• Detects patterns like "high memory → GC overhead → out of memory"
• Predicts time to failure
• Suggests preventive actions before impact occurs

Correlation Rule Categories

Infrastructure Patterns

Network Partition Detection

• Identifies split-brain scenarios in distributed systems
• Correlates cluster communication issues across nodes
• Triggers when 30% or more nodes are affected
• Provides immediate infrastructure team notification

Periodic Failure Recognition

• Detects failures that occur on regular schedules
• Identifies patterns in hourly, daily, or weekly cycles
• Links issues to scheduled jobs or batch processes
• Requires minimum 3 occurrences for pattern confirmation

Application Patterns

Service Dependency Tracking

• Uses discovered CMDB relationships
• Correlates events across dependent services
• Propagates root cause analysis downstream
• Maintains confidence scores for correlation accuracy

Performance Degradation

• Tracks severity escalation patterns (warning → major → critical)
• Correlates performance metrics with service health
• Identifies gradual degradation before failure
• 30-minute analysis window for trend detection

How Correlation Rules Work

Pattern Matching Process

The correlation engine evaluates multiple conditions for each incoming event:

1. Event Pattern Analysis

   • Searches for keywords and patterns in event titles and descriptions
   • Maintains 90% confidence for exact pattern matches
   • Case-insensitive matching for flexibility

2. Temporal Correlation

   • Analyzes events that follow each other in sequence
   • Configurable time windows (typically 5 minutes)
   • Confidence increases with more correlated events

3. Topology-Based Analysis

   • Leverages CMDB relationships to find related infrastructure
   • Traces dependencies upstream and downstream
   • Identifies root causes based on propagation direction

4. Trend Detection

   • Monitors metric trends over extended periods
   • Detects increasing or decreasing patterns
   • Calculates time to threshold breach

Business Impact Assessment

Automated Impact Analysis

Every correlation rule includes business impact evaluation:

Critical Impact

• Payment processing failures
• Authentication service outages
• Data integrity issues
• Customer-facing service disruptions

High Impact

• Performance degradation affecting user experience
• Partial service availability
• Backup system failures
• Compliance monitoring gaps

Medium Impact

• Internal service slowdowns
• Non-critical batch job failures
• Development environment issues
• Monitoring system alerts

Root Cause Identification

The system automatically determines root causes through:

Upstream Analysis

• Traces failures to originating service
• Identifies first critical event in sequence
• Maps dependency chains
• Calculates confidence scores

Timeline Reconstruction

• Orders events chronologically
• Identifies trigger events
• Maps cascade patterns
• Highlights preventable failures

Remediation Suggestions

Intelligent Recommendations

Based on detected patterns, the system provides specific remediation guidance:

Database Issues

• Increase connection pool size
• Optimize slow queries
• Add database replicas
• Implement connection pooling

Service Failures

• Restart affected services
• Scale out infrastructure
• Activate circuit breakers
• Redirect traffic to healthy instances

Resource Exhaustion

• Clear caches
• Restart memory-leaking applications
• Increase resource allocations
• Implement auto-scaling policies

Pattern Learning

The correlation engine continuously improves through:

Historical Analysis

• Reviews past incident patterns
• Identifies successful remediation actions
• Builds pattern library
• Improves detection accuracy

Feedback Integration

• Learns from operator actions
• Adjusts confidence thresholds
• Updates correlation rules
• Refines root cause analysis

Performance & Scalability

Processing Capabilities

Real-Time Analysis

• Evaluates rules within milliseconds
• Processes thousands of events per minute
• Maintains sub-second correlation latency
• Scales horizontally for high volumes

Correlation Accuracy

• 90%+ pattern match accuracy
• Confidence scoring for all correlations
• False positive rate below 5%
• Continuous accuracy improvement

Rule Evaluation Metrics

Metric	Performance	Description
Rule Evaluation Speed	< 100ms	Time to evaluate single rule
Pattern Matching	< 50ms	Pattern search performance
Correlation Window	5-30 min	Configurable time windows
Confidence Threshold	0.7	Minimum score for correlation
Historical Lookup	20 events	Past events analyzed per rule

Implementation Best Practices

Getting Started

Phase 1: Pattern Discovery

• Enable correlation rules engine
• Monitor detected patterns for accuracy
• Review suggested correlations
• Validate root cause identification

Phase 2: Rule Refinement

• Adjust confidence thresholds
• Customize time windows
• Define business impact mappings
• Configure team notifications

Phase 3: Optimization

• Analyze correlation effectiveness
• Fine-tune pattern detection
• Expand rule coverage
• Integrate with workflows

Correlation Strategy

Start Simple

• Begin with infrastructure patterns
• Focus on critical services
• Validate correlations manually
• Build confidence gradually

Expand Coverage

• Add application-specific patterns
• Include business service context
• Implement predictive patterns
• Enable proactive detection

Continuous Improvement

• Review correlation accuracy monthly
• Update patterns based on new incidents
• Refine root cause detection
• Enhance remediation suggestions

Use Case Examples

Database Outage Correlation

Scenario: Database connection pool exhaustion

Detection:

• Initial event: "Connection pool limit reached"
• Correlated events: Multiple "connection timeout" errors
• Time window: 5 minutes
• Confidence: 95%

Analysis:

• Root cause: Database connection pool exhaustion
• Business impact: High - affects all database-dependent services
• Affected services: 12 applications identified through topology

Remediation:

• Immediate: Increase connection pool size
• Short-term: Restart connection pool
• Long-term: Optimize connection usage patterns

Cascading Microservice Failure

Scenario: Payment service failure affecting multiple systems

Detection:

• Pattern: Service dependency cascade
• Propagation: Downstream from payment service
• Severity escalation: Warning → Major → Critical
• Time span: 3 minutes

Analysis:

• Root cause: Payment gateway timeout
• Cascade path: Payment → Orders → Inventory → Notifications
• Business impact: Critical - revenue impact

Remediation:

• Circuit breaker activation
• Traffic redirection to backup gateway
• Service restart sequence
• Cache warming after recovery

Memory Leak Prevention

Scenario: Gradual memory increase in application

Detection:

• Trend: Increasing memory usage over 1 hour
• Pattern sequence: High memory → GC overhead warnings
• Prediction: Out of memory in 45 minutes
• Confidence: 85%

Analysis:

• Root cause: Memory leak in order processing service
• Impact timeline: Failure predicted in 45 minutes
• Affected users: Estimated 5,000 if failure occurs

Remediation:

• Preventive restart during low traffic
• Heap dump collection for analysis
• Temporary traffic reduction
• Development team notification

Benefits & ROI

Operational Efficiency

Noise Reduction

• 85% fewer individual alerts through correlation
• Single incident view for related events
• Reduced alert fatigue for operations teams
• Focus on root causes, not symptoms

Faster Resolution

• 70% reduction in MTTR through root cause identification
• Immediate remediation suggestions
• Automated pattern recognition
• Historical pattern matching

Proactive Prevention

• Predict failures before impact
• Early warning for resource exhaustion
• Trend-based alerting
• Preventive action recommendations

Business Value

Benefit	Typical Improvement	Annual Value
Reduced Incidents	30% fewer outages	$500K-2M saved
Faster Recovery	70% MTTR reduction	1,000+ hours saved
Alert Reduction	85% less noise	50% efficiency gain
Pattern Detection	95% accuracy	Continuous improvement

Next Steps

• 📖 Event Correlation - Deep dive into correlation strategies
• 📖 AI Analysis - Enhance rules with AI insights
• 📖 Notification Channels - Configure alert routing