Intelligent Event Correlation

Transform thousands of alerts into meaningful incidents with NopeSight's multi-strategy correlation engine. Our platform automatically groups related events, identifies root causes, and reduces alert noise by up to 90%.

How Correlation Works

NopeSight employs four parallel correlation strategies that work together to ensure no related events are missed:

The Four Correlation Strategies

1. Temporal Correlation (Time-Based)

How it works: Events occurring within a 5-minute sliding window are automatically evaluated for correlation. The system intelligently groups events based on time proximity, with closer events receiving higher correlation scores.

Key Features:

• Configurable time windows (default: 5 minutes)
• Automatic burst detection for rapid-fire alerts
• Storm suppression to prevent alert floods
• Sequence pattern recognition

Real-World Example: When a database fails at 10:00 AM, all application errors occurring between 9:55 AM and 10:05 AM are automatically grouped into a single incident, showing the complete impact timeline.

Benefits:

• Reduces alert storms to single incidents
• Provides complete event timeline
• Identifies cascading failures
• Preserves event sequencing

2. Topology-Based Correlation (CMDB Intelligence)

How it works: Leverages your discovered infrastructure relationships from the CMDB to understand service dependencies. When an event occurs, the system automatically checks related configuration items for correlated issues.

Key Features:

• Uses discovered CI relationships
• Traverses dependencies up to 3 levels deep
• 10-minute correlation window for infrastructure events
• Critical relationships receive higher correlation scores (0.9 vs 0.7)

Relationship Types Analyzed:

• Runs On - Applications on servers
• Depends On - Service dependencies
• Connects To - Network connections
• Hosted By - Virtual infrastructure

Real-World Example:

When a database server fails, the system automatically correlates:

• Application connection errors
• Web server timeout alerts
• Load balancer health check failures
• All dependent service alerts

Benefits:

• Automatically maps impact across infrastructure
• Identifies true root cause vs symptoms
• Understands complex service dependencies
• Reduces troubleshooting time dramatically

3. Pattern-Based Correlation (Signature Matching)

How it works: Events with identical correlation signatures are automatically grouped together. The system generates MD5-based signatures from event characteristics and matches them across a 1-hour lookback window.

Key Features:

• Signature-based exact matching
• Highest confidence score (0.9) for pattern matches
• 1-hour pattern recognition window
• Automatic deduplication

Pattern Recognition Examples:

• Recurring application errors with same stack trace
• Identical network connectivity issues
• Repeated authentication failures
• Similar performance degradation patterns

Benefits:

• Eliminates duplicate incidents
• Recognizes recurring issues instantly
• Groups identical problems across systems
• Maintains high precision in correlation

4. Service-Based Correlation (Business Context)

How it works: Groups events affecting the same business service or application, regardless of the underlying infrastructure. Uses a 15-minute window to catch service-related issues.

Key Features:

• Service and application-aware grouping
• 15-minute correlation window
• 0.8 correlation score for service matches
• Business impact consideration

Service Correlation in Action: When your payment service experiences issues:

• Payment gateway timeouts
• Database transaction failures
• API response delays
• Customer-facing error messages

All get correlated into a single "Payment Service Degradation" incident.

Benefits:

• Business service visibility
• Application-centric correlation
• Cross-infrastructure grouping
• Service impact understanding

Correlation Scoring & Merging

How Scores Are Calculated

Each correlation strategy produces a confidence score between 0 and 1:

Strategy	Score Range	Threshold	Weight
Temporal	0.5 - 1.0	> 0.5	Time proximity based
Topology	0.6 - 0.9	> 0.6	Relationship criticality
Pattern	0.9	Exact match	Highest confidence
Service	0.8	Service match	Fixed score

Intelligent Merging Process

When an event matches multiple strategies:

1. Maximum score wins - Takes the highest confidence from all strategies
2. Reason tracking - Records which strategies matched
3. Evidence collection - Gathers supporting data from each strategy
4. Final threshold - Events with final score > 0.7 are correlated

Root Cause Analysis

Automatic Root Cause Identification

The correlation engine automatically identifies the most likely root cause using multiple techniques:

1. Temporal Analysis

• Earliest critical/major event in the correlation group
• Events that triggered the cascade

2. Topology Traversal

• Infrastructure dependencies analysis
• Service relationship mapping
• Impact propagation tracking

3. Pattern Recognition

• Historical resolution patterns
• Previous root cause data
• Known issue signatures

Root Cause Confidence

Each identified root cause includes:

• Confidence Score (0-100%)
• Supporting Evidence
• Impact Chain Visualization
• Remediation Suggestions

Correlation Groups & Management

Correlation Group Features

When events are correlated, the system:

Creates Unified Incident View:

• Single correlation ID for all related events
• Complete timeline of the incident
• Aggregated severity and impact
• Combined business context

Provides Analysis:

• Event count and distribution
• Time span of the incident
• Severity breakdown
• Affected CI listing
• Common patterns identification

Enables Smart Actions:

• Single notification for event group
• Consolidated ticket creation
• Grouped remediation actions
• Unified reporting

Group Lifecycle Management

Performance & Optimization

Scalability Features

High-Volume Handling:

• Processes thousands of events per minute
• Parallel correlation strategies
• Efficient scoring algorithms
• Optimized database queries

Performance Metrics:

Metric	Typical Performance
Correlation Latency	< 1 second
Events per Second	250+
Correlation Accuracy	92-95%
False Positive Rate	< 3%

Continuous Learning

The correlation engine improves over time through:

Pattern Learning:

• Learns from resolved incidents
• Identifies new correlation patterns
• Adjusts confidence scores
• Updates signature library

Feedback Integration:

• Manual correlation corrections
• False positive identification
• Root cause validation
• Resolution pattern tracking

Best Practices

Optimization Guidelines

1. Start Simple

• Begin with temporal correlation
• Add topology correlation using CMDB
• Enable pattern matching after baseline
• Fine-tune service correlation last

2. Window Tuning

• Monitor correlation accuracy
• Adjust time windows based on your environment
• Consider infrastructure response times
• Account for geographic distribution

3. Score Thresholds

• Start with default 0.7 threshold
• Increase if over-correlation occurs
• Decrease for tighter correlation
• Monitor false positive rates

4. CMDB Accuracy

• Ensure CI relationships are current
• Validate dependency mappings
• Regular discovery updates
• Clean obsolete relationships

Use Case Examples

Example 1: Database Outage

Scenario: Primary database server fails

Events Generated:

• Database unreachable (Critical)
• 15 application connection errors (Major)
• 3 web server timeouts (Major)
• 50+ user session errors (Warning)
• Load balancer health check failures (Major)

Correlation Result:

• Single Incident: "Database Outage Affecting Production"
• Root Cause: Database server hardware failure
• Correlated: 70 events → 1 incident
• Strategies Used: Topology (0.9), Temporal (0.8), Service (0.8)
• Time to Correlate: 0.3 seconds

Example 2: Network Switch Failure

Scenario: Core network switch experiences intermittent failures

Events Generated:

• Switch port flapping alerts
• 200+ device connectivity alerts
• Application timeout errors
• Service degradation warnings

Correlation Result:

• Single Incident: "Core Switch SW-01 Instability"
• Root Cause: Switch firmware bug
• Correlated: 247 events → 1 incident
• Strategies Used: Topology (0.9), Pattern (0.9), Temporal (0.7)
• Time to Correlate: 0.5 seconds

Example 3: Application Memory Leak

Scenario: Application gradually consuming memory over 2 hours

Events Generated:

• Memory usage warnings (every 10 min)
• GC duration increasing alerts
• Response time degradation
• Eventually: OutOfMemory error

Correlation Result:

• Single Incident: "Application Memory Exhaustion"
• Root Cause: Memory leak in payment service
• Correlated: 15 events → 1 incident
• Strategies Used: Service (0.8), Pattern (0.9), Temporal (0.6)
• Prediction: System predicted failure 90 minutes before crash

Integration with Other Features

AI Analysis Integration

Correlated events are automatically sent for:

• Anomaly scoring
• Predictive analysis
• Resolution suggestions
• Impact assessment

Automation Integration

Correlation groups trigger:

• Automated remediation workflows
• Intelligent notification routing
• Priority-based escalation
• Runbook execution

Business Service Mapping

Correlations are enriched with:

• Business service context
• Customer impact analysis
• SLA tracking
• Revenue impact calculation

Measuring Success

Key Performance Indicators

Track correlation effectiveness with these metrics:

KPI	Target	Description
Noise Reduction	> 85%	Percentage of events correlated
Correlation Accuracy	> 90%	Correctly grouped events
False Positive Rate	< 5%	Incorrectly correlated events
Root Cause Accuracy	> 80%	Correct root cause identification
Time to Correlate	< 1 sec	Processing latency
MTTD Improvement	50% reduction	Faster problem detection

Continuous Improvement

Monthly Review:

• Analyze correlation patterns
• Review false positives
• Adjust thresholds
• Update pattern library

Quarterly Optimization:

• CMDB relationship audit
• Correlation strategy tuning
• Performance optimization
• Machine learning model updates

Next Steps

• 📖 AI Analysis - Enhance correlations with AI insights
• 📖 Automation Rules - Automate responses to correlations
• 📖 Notification Channels - Configure alert routing