data-visualization-guide.md

Data Visualization Guide

This guide explains how to create visualizations, explore sensor data through charts, and use advanced analytics features.

Overview

The Data Visualization page provides comprehensive tools to:

• Create interactive charts for sensor data
• Analyze correlations between sensors
• Perform time series analysis
• Detect anomalies and trends
• Generate statistical summaries
• Export visualizations

Accessing Data Visualization

1. Open the application at http://localhost:5173
2. Click Data Visualization in the sidebar or home page
3. Select a machine group from the dropdown

Selecting Sensors for Analysis

Step 1: Choose Machine Group

1. Select a machine group from the Select Machine Group dropdown
2. The system loads available sensors automatically

Step 2: Select Sensors

1. Use the sensor selector to choose sensors for visualization
2. You can select multiple sensors for comparison
3. Selected sensors appear in the visualization

Tips:

• Start with 2-3 sensors for clarity
• Select related sensors (e.g., all pressure sensors)
• Use sensor categories to guide selection

Step 3: Apply Date Range Filter (Optional)

1. Set Date From to filter start date
2. Set Date To to filter end date
3. Click Apply Filter to update visualizations
4. Use Clear Filter to remove date restrictions

Date Range Benefits:

• Focus on specific time periods
• Improve performance for large datasets
• Analyze seasonal patterns

Chart Types

Time Series Charts

Purpose: Show sensor values over time

Features:

• Multiple sensors on same chart
• Interactive tooltips showing exact values
• Zoom and pan capabilities
• Legend to toggle sensor visibility

When to use:

• Track trends over time
• Compare multiple sensors
• Identify patterns and cycles

Correlation Analysis

Purpose: Understand relationships between sensors

Features:

• Correlation matrix heatmap
• Correlation coefficients (-1 to +1)
• Strong correlations highlighted

Interpretation:

• +1.0: Perfect positive correlation
• 0.0: No correlation
• -1.0: Perfect negative correlation
• >0.7 or <-0.7: Strong correlation

When to use:

• Find related sensors
• Understand sensor dependencies
• Detect redundant measurements

Advanced Analytics

Summary Statistics

Provides statistical overview for each sensor:

• Count: Number of data points
• Mean: Average value
• Standard Deviation: Data spread
• Min/Max: Minimum and maximum values
• Quartiles: 25th, 50th (median), 75th percentiles

Access:

1. Select sensors
2. Click Summary Stats in Advanced Analytics
3. View statistics table

Use cases:

• Quick data overview
• Identify outliers
• Understand data distribution

Time Series Analysis

Advanced time series features:

• Rolling Mean: Moving average over time window
• Rolling Standard Deviation: Volatility over time
• Trend Detection: Identify upward/downward trends
• Seasonal Patterns: Detect recurring patterns

Access:

1. Select sensors
2. Click Time Series in Advanced Analytics
3. View time series charts with trend lines

Use cases:

• Identify long-term trends
• Detect seasonal patterns
• Understand data volatility

Distribution Plots

Histogram: Shows value distribution

• Bins: Number of intervals
• Frequency: Count of values in each bin
• Density: Normalized frequency

Access:

1. Select sensors
2. Click Distribution in Advanced Analytics
3. View histogram charts

Interpretation:

• Normal distribution: Bell-shaped curve
• Skewed: Asymmetric distribution
• Bimodal: Two peaks (may indicate two states)

Use cases:

• Understand value distribution
• Detect data quality issues
• Identify normal operating ranges

Box Plots

Purpose: Show data distribution and outliers

Components:

• Box: Interquartile range (25th to 75th percentile)
• Median Line: Middle value
• Whiskers: Data range (excluding outliers)
• Outliers: Points beyond whiskers

Access:

1. Select sensors
2. Click Box Plot in Advanced Analytics
3. View box plot visualization

Interpretation:

• Box size: Data spread
• Outliers: Unusual values
• Median position: Data center

Use cases:

• Compare sensor distributions
• Identify outliers
• Understand data variability

Seasonal Decomposition

Purpose: Break down time series into components

Components:

• Trend: Long-term direction
• Seasonal: Recurring patterns
• Residual: Random variation

Access:

1. Select sensors
2. Click Seasonal in Advanced Analytics
3. View decomposition charts

Use cases:

• Understand seasonal patterns
• Separate trend from seasonality
• Forecast future values

Anomaly Detection

Purpose: Identify unusual data points

Method: Z-score based detection

• Z-score: Number of standard deviations from mean
• Threshold: Default 2.0 (configurable)
• Anomalies: Points beyond threshold

Access:

1. Select sensors
2. Click Anomalies in Advanced Analytics
3. View anomaly detection results

Interpretation:

• Red points: Detected anomalies
• Z-score > 2: Unusually high
• Z-score < -2: Unusually low

Use cases:

• Detect sensor malfunctions
• Identify unusual events
• Quality control

Working with Visualizations

Interactive Features

• Hover: See exact values in tooltips
• Zoom: Click and drag to zoom in
• Pan: Drag to move around zoomed view
• Reset: Click reset to return to full view
• Toggle: Click legend items to show/hide sensors

Exporting Visualizations

Export Options:

• Screenshot: Use browser screenshot tools
• Data Export: Export underlying data as CSV
• Print: Use browser print function

Tips:

• Use full-screen mode for better screenshots
• Export data for external analysis tools
• Save important visualizations for reports

Best Practices

1. Start Simple: Begin with time series charts
2. Select Relevant Sensors: Choose sensors related to your analysis
3. Use Date Filters: Focus on specific time periods
4. Compare Related Sensors: Group by category (pressure, temperature, etc.)
5. Check Multiple Views: Use different chart types for comprehensive understanding
6. Interpret Statistics: Understand what metrics mean for your use case

Analysis Workflow

Recommended Workflow

1. Select Sensors: Choose sensors of interest
2. View Time Series: Understand overall trends
3. Check Summary Stats: Get statistical overview
4. Analyze Correlations: Find relationships
5. Detect Anomalies: Identify unusual values
6. Review Distribution: Understand value ranges
7. Export Results: Save for reporting

Example Analysis

Scenario: Analyzing pressure sensor data

1. Select all pressure sensors
2. View time series to see trends
3. Check correlation to find related sensors
4. Use box plots to compare distributions
5. Detect anomalies for quality issues
6. Export findings for report

Performance Considerations

Large Datasets

• Use date range filters to reduce data volume
• Select fewer sensors for faster rendering
• Consider using preprocessed/aggregated data

Optimization Tips

• Limit sensor selection to essential ones
• Use appropriate date ranges
• Allow charts to fully load before interacting

Troubleshooting

Charts Not Displaying

Problem: No charts appear after selecting sensors Solutions:

• Check data is loaded (see Data Loading Guide)
• Verify sensors have data in selected date range
• Check browser console for errors
• Refresh the page

Slow Performance

Problem: Charts load slowly Solutions:

• Reduce date range
• Select fewer sensors
• Use aggregated data source
• Check network connection

Missing Analytics

Problem: Advanced analytics not available Solutions:

• Ensure sensors are selected
• Check data has sufficient points
• Verify backend service is running
• Review error messages

Next Steps

After creating visualizations:

1. Assess Quality: Use Data Quality Guide for comprehensive assessment
2. Find Missing Values: Check Missing Values Guide
3. Detect Invalid Values: See Invalid Values Guide
4. Chat with Agent: Use DQA Agent Guide for insights

Related Documentation

• Data Loading Guide - Select and load data
• Data Quality Guide - Comprehensive quality assessment
• Missing Values Guide - Missing data analysis

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For API details, see the Backend API Documentation.