Visualizing Streaming Data

Preface
Who This Book Will Benefit
How This Book Is Organized
Conventions Used in This Book
Using Code Examples
O’Reilly Safari
How to Contact Us
Acknowledgments

1. Introduction
Why Visualizations
The Standard
Terms
Data Formats
Data Visualization Applications
Assumptions and Setup

2. Goals
Presentation Goals
Pre-batch Analysis
The Analyst Decision Queue
Data Pipeline Visualization
Show Movement on a Map
Asking New Questions
Seeing Frequency and Order

3. Data Sources
Data Source Types
What to Stream
Data Storage Considerations
Managing Multiple Sources

4. Streaming Your Data
How to Stream Data
Buffering
Streaming Best Practices

5. Processing Streaming Data for Visualization
Batch Processing
Inline Processing
Processing Patterns
Lookups
Lookup Types
Normalizing Events
Extracting Value
The JSON Collection Decorator
Processing Checklist
Streaming Statistics
Types of Statistics
Record Context Checklist
Scaling Data Streams
Presenting Processing

6. Developing a Client
Native or Browser Development
Frameworks and Libraries
A Common Approach
Getting Started with the Sample Client Application
Client Libraries
Code Structure
Alternative Approaches

7. Presenting Streaming Data
Showing Streaming Data
Events
Logs
Records
Dashboards
Visual Elements and Properties
Data Density
Dividing Time
Time to Live
Context
Visual Language
Appropriate Displays

8. Visualization Components
Records
Statistics
Visualizations
Streaming Options for Common Visualizations
Streaming Visualization Techniques
Bar Chart Example
Static Information

9. Streaming Analysis
Visual Distractions
Visual Deception
Cognitive Bias
Analysis Models
Visual Analysis
Streaming Analysis Workflow
Context Awareness
Outliers Example

10. Workflow Visualization
Updating Processing
Interacting with Visualizations
Storing Decisions

11. Streaming Data Dashboard
Layout
Flow Direction
Component Movement
Autopilot

12. Machine Learning
Machine Learning Primer
Machine Learning and Streaming Data Visualization
Presenting Machine Learning Results
Supervised Learning and Continuous Tuning
Presenting the Unexpected
Machine Learning Decisions on What to Display

13. Collaboration
Why Collaborate
Sharing Out

14. Exports
Configurations
Datasets
Streaming Replay Reports
Static Reports
Submitting Processing Updates for the Data Feed

15. Use Cases
Security
Machine Learning Interaction
Smart Devices (aka the Internet of Things)
Brand Monitoring
Public Opinion
Application Data
Error Monitoring
Collaboration
Workflow
Analyst Input
Data Exploration
Examples
Powerboard
Vizceral
Alooma Live
Stream-Viz

16. Summary and References
Links Mentioned
Data
Transform and Filter
Presentation Dashboards and Components
Interactions and Actions
Beyond the System

Index