Event Automation & Correlation Rules

Transform your event management from reactive to proactive with Tripl-i'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

Tripl-i'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