Data Charts And Graphs

Data Charts and Graphs: Visual Data Analysis

Data Charts and Graphs provide visual representations of your time-series data, enabling you to understand trends, identify patterns, and analyze the data collected from your sensors and devices directly within the Krill application.

Overview

While Data Points store raw time-series data, Graph nodes transform that data into visual insights. By attaching a Graph node to a Data Point, you can instantly visualize historical trends, spot anomalies, and gain a deeper understanding of your system’s behavior over time.

Key Features

• Time-Series Visualization: Display data points over time
• Multiple Chart Types: Line charts, area charts, and more
• Configurable Time Ranges: View minutes, hours, days, or custom periods
• Real-Time Updates: Charts update as new data arrives
• Interactive Display: Zoom, pan, and explore your data
• Historical Analysis: Access stored time-series data for review
• Parent Integration: Automatically inherits data from parent Data Point

How It Works

Graph nodes operate as read-only visualization components:

1. Configuration: Create Graph node as child of a Data Point
2. Data Retrieval: Queries parent Data Point’s time-series storage
3. Rendering: Transforms data into visual chart representation
4. Display: Shows chart in Krill App interface
5. Updates: Refreshes as new data is stored to parent

Use Cases

• Sensor Monitoring: Visualize temperature, humidity, pressure over time
• Trend Analysis: Identify patterns in equipment performance
• Anomaly Detection: Spot unusual readings visually
• Performance Review: Analyze system efficiency over periods
• Reporting: Visual data for status reports and presentations
• Debugging: Trace data flow issues through visual inspection
• Capacity Planning: Understand resource utilization patterns

Example Configurations

Temperature Monitoring:

Temperature Sensor (Serial Device)
  └─> Temperature (Data Point)
      └─> Temperature Graph (Graph)
          - Shows 24-hour temperature history
          - Highlights high/low thresholds

Multi-Metric Dashboard:

Environmental Station
  ├─> Temperature (Data Point)
  │   └─> Temp Chart (Graph)
  ├─> Humidity (Data Point)
  │   └─> Humidity Chart (Graph)
  └─> Pressure (Data Point)
      └─> Pressure Chart (Graph)

Energy Consumption Analysis:

Power Meter
  └─> Power Usage (Data Point)
      ├─> Real-Time Graph (last hour)
      └─> Historical Graph (last 7 days)

Integration with Data Points

Graphs are designed to work seamlessly with Data Points:

Data Type	Visualization
DOUBLE	Line/area charts with numeric Y-axis
BOOL	Step chart showing ON/OFF states
TEXT	Event timeline markers
JSON	Parsed numeric values charted

Visualization Best Practices

• Appropriate Time Ranges: Match chart time range to data characteristics
• Meaningful Labels: Use descriptive names and units
• Threshold Lines: Add visual reference lines for limits
• Data Density: Balance detail vs. readability
• Color Coding: Use consistent colors across related charts
• Update Frequency: Match chart refresh to data collection rate

Chart Types

Chart Type	Best For	Example Use
Line Chart	Continuous measurements	Temperature, pressure
Area Chart	Cumulative values	Energy consumption
Step Chart	Discrete states	On/off status, digital inputs

Performance Considerations

• Data Sampling: Large time ranges may sample data for performance
• Render Optimization: Charts optimized for smooth display
• Memory Efficiency: Only loads visible data range
• Background Updates: Minimal impact on system performance

Integration Points

• Data Points: Primary data source for visualization
• Triggers: Visualize threshold crossings
• Compute: Chart computed statistical summaries
• Calculation: Display derived values
• Serial Devices: Graph raw sensor feeds

Example Workflows

Real-Time Monitoring:

1. Serial Device reads sensor every second
2. Data Point stores time-series data
3. Graph displays last 30 minutes
4. Visual anomaly detection

Historical Analysis:

1. Data Point accumulates weeks of data
2. Graph shows weekly trends
3. Identify recurring patterns
4. Plan maintenance schedules

Comparative Analysis:

1. Multiple Data Points from different sensors
2. Each with its own Graph
3. Side-by-side comparison
4. Cross-correlation identification

Data Charts and Graphs transform raw numbers into actionable visual insights, making it easy to understand what your sensors and devices are telling you.