SapienStream

Real-time Analytics

Planned

Instant insights from your manufacturing data. Currently in planning phase with foundational data infrastructure ready for real-time analytics implementation.

Foundation Infrastructure

Data Collection Ready
Implemented
Core data models and collection mechanisms are operational and ready for analytics.
Machine data models
Tag value storage system
Timestamp tracking
Quality indicators
Real-time Processing
Planned
Stream processing engine for live data analysis and instant alerting capabilities.
Live data streaming
Event processing engine
Sliding window calculations
Complex event correlation

Planned Analytics Capabilities

Live Dashboards
Planned
Real-time visualization of manufacturing metrics and KPIs.
Machine status indicators
Production rate meters
Quality trending charts
Energy consumption graphs
OEE calculations
Intelligent Alerting
Planned
Smart notification system with configurable thresholds and escalation.
Threshold-based alerts
Anomaly detection
Multi-channel notifications
Alert escalation workflows
Historical alert analysis
Performance Analytics
Planned
Deep dive into equipment performance and production efficiency metrics.
Cycle time analysis
Throughput optimization
Downtime categorization
Energy efficiency tracking
Predictive maintenance insights
Quality Monitoring
Planned
Real-time quality analysis and statistical process control monitoring.
SPC chart generation
Control limit monitoring
Defect rate tracking
Process capability studies
Quality trend analysis

Current Data Access

Available Data for Analytics
While we build real-time analytics, you can access the underlying data through our API
# Get machine performance data
curl https://sapienstream.com/api/machines/machine_001/tags \
  -H "Authorization: Bearer your_jwt_token"

# Response includes all current tag values
{
  "machine_id": "machine_001",
  "tags": [
    {
      "name": "cycle_time",
      "value": 45.2,
      "unit": "seconds",
      "quality": "GOOD",
      "timestamp": "2024-01-15T10:30:00Z"
    },
    {
      "name": "temperature",
      "value": 185.5,
      "unit": "celsius",
      "quality": "GOOD",
      "timestamp": "2024-01-15T10:30:00Z"
    },
    {
      "name": "pressure",
      "value": 1250.0,
      "unit": "psi",
      "quality": "GOOD",
      "timestamp": "2024-01-15T10:30:00Z"
    }
  ]
}

# Get semantic tag insights
curl https://sapienstream.com/api/semantic-tags/quality_control \
  -H "Authorization: Bearer your_jwt_token"

# Search for specific data patterns
curl "https://sapienstream.com/api/semantic-tags/search?q=temperature" \
  -H "Authorization: Bearer your_jwt_token"

Development Timeline

Real-time Analytics Roadmap
Our planned approach to building comprehensive analytics capabilities

Phase 1: Streaming Foundation (Foundation)

Build the core streaming infrastructure for real-time data processing.

• WebSocket streaming endpoints
• Event processing engine
• Time-series data storage
• Basic alerting framework

Phase 2: Visualization Layer (Next)

Create interactive dashboards and real-time visualization components.

• Live dashboard framework
• Chart and graph components
• KPI calculation engine
• Custom widget builder

Phase 3: Intelligent Analytics (Future)

Add machine learning and advanced analytics capabilities.

• Anomaly detection algorithms
• Predictive analytics models
• Statistical process control
• Pattern recognition systems

Phase 4: Advanced Features (Advanced)

Implement sophisticated analytics and optimization capabilities.

• Digital twin analytics
• Optimization recommendations
• Multi-site comparative analysis
• Industry benchmarking

Planned Technical Architecture

Analytics Stack
Technologies and frameworks we're planning to implement

Data Processing

Apache Kafka
Redis Streams
TimescaleDB
InfluxDB

Analytics Engine

Apache Spark
Pandas/NumPy
Scikit-learn
TensorFlow

Visualization

D3.js
Chart.js
Plotly
Grafana

Foundation Ready

While real-time analytics are in development, the foundation is solid:

All manufacturing data is being collected and stored
API endpoints provide access to current and historical data
Data models support time-series analysis
Quality indicators and metadata are tracked