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Beautiful Data

Cover of Beautiful Data by Jeff Hammerbacher... Published by O'Reilly Media, Inc.
  1. Beautiful Data
    1. SPECIAL OFFER: Upgrade this ebook with O’Reilly
    2. A Note Regarding Supplemental Files
    3. Preface
      1. How This Book Is Organized
      2. Conventions Used in This Book
      3. Using Code Examples
      4. How to Contact Us
      5. Safari® Books Online
    4. 1. Seeing Your Life in Data
      1. Personal Environmental Impact Report (PEIR)
      2. your.flowingdata (YFD)
      3. Personal Data Collection
      4. Data Storage
      5. Data Processing
      6. Data Visualization
      7. The Point
      8. How to Participate
    5. 2. The Beautiful People: Keeping Users in Mind When Designing Data Collection Methods
      1. Introduction: User Empathy Is the New Black
      2. The Project: Surveying Customers About a New Luxury Product
      3. Specific Challenges to Data Collection
      4. Designing Our Solution
      5. Results and Reflection
    6. 3. Embedded Image Data Processing on Mars
      1. Abstract
      2. Introduction
      3. Some Background
      4. To Pack or Not to Pack
      5. The Three Tasks
      6. Slotting the Images
      7. Passing the Image: Communication Among the Three Tasks
      8. Getting the Picture: Image Download and Processing
      9. Image Compression
      10. Downlink, or, It's All Downhill from Here
      11. Conclusion
    7. 4. Cloud Storage Design in a PNUTShell
      1. Introduction
      2. Updating Data
      3. Complex Queries
      4. Comparison with Other Systems
      5. Conclusion
      6. Acknowledgments
      7. References
    8. 5. Information Platforms and the Rise of the Data Scientist
      1. Libraries and Brains
      2. Facebook Becomes Self-Aware
      3. A Business Intelligence System
      4. The Death and Rebirth of a Data Warehouse
      5. Beyond the Data Warehouse
      6. The Cheetah and the Elephant
      7. The Unreasonable Effectiveness of Data
      8. New Tools and Applied Research
      9. MAD Skills and Cosmos
      10. Information Platforms As Dataspaces
      11. The Data Scientist
      12. Conclusion
    9. 6. The Geographic Beauty of a Photographic Archive
      1. Beauty in Data: Geograph
      2. Visualization, Beauty, and Treemaps
      3. A Geographic Perspective on Geograph Term Use
      4. Beauty in Discovery
      5. Reflection and Conclusion
      6. Acknowledgments
      7. References
    10. 7. Data Finds Data
      1. Introduction
      2. The Benefits of Just-in-Time Discovery
      3. Corruption at the Roulette Wheel
      4. Enterprise Discoverability
      5. Federated Search Ain't All That
      6. Directories: Priceless
      7. Relevance: What Matters and to Whom?
      8. Components and Special Considerations
      9. Privacy Considerations
      10. Conclusion
    11. 8. Portable Data in Real Time
      1. Introduction
      2. The State of the Art
      3. Social Data Normalization
      4. Conclusion: Mediation via Gnip
    12. 9. Surfacing the Deep Web
      1. What Is the Deep Web?
      2. Alternatives to Offering Deep-Web Access
      3. Conclusion and Future Work
      4. References
    13. 10. Building Radiohead's House of Cards
      1. How It All Started
      2. The Data Capture Equipment
      3. The Advantages of Two Data Capture Systems
      4. The Data
      5. Capturing the Data, aka "The Shoot"
      6. Processing the Data
      7. Post-Processing the Data
      8. Launching the Video
      9. Conclusion
    14. 11. Visualizing Urban Data
      1. Introduction
      2. Background
      3. Cracking the Nut
      4. Making It Public
      5. Revisiting
      6. Conclusion
    15. 12. The Design of
      1. Visualization and Social Data Analysis
      2. Data
      3. Visualization
      4. Collaboration
      5. Voyagers and Voyeurs
      6. Conclusion
      7. References
    16. 13. What Data Doesn't Do
      1. When Doesn't Data Drive?
      2. Conclusion
      3. References
    17. 14. Natural Language Corpus Data
      1. Word Segmentation
      2. Secret Codes
      3. Spelling Correction
      4. Other Tasks
      5. Discussion and Conclusion
      6. Acknowledgments
    18. 15. Life in Data: The Story of DNA
      1. DNA As a Data Store
      2. DNA As a Data Source
      3. Fighting the Data Deluge
      4. The Future of DNA
      5. Acknowledgments
    19. 16. Beautifying Data in the Real World
      1. The Problem with Real Data
      2. Providing the Raw Data Back to the Notebook
      3. Validating Crowdsourced Data
      4. Representing the Data Online
      5. Closing the Loop: Visualizations to Suggest New Experiments
      6. Building a Data Web from Open Data and Free Services
      7. Acknowledgments
      8. References
    20. 17. Superficial Data Analysis: Exploring Millions of Social Stereotypes
      1. Introduction
      2. Preprocessing the Data
      3. Exploring the Data
      4. Age, Attractiveness, and Gender
      5. Looking at Tags
      6. Which Words Are Gendered?
      7. Clustering
      8. Conclusion
      9. Acknowledgments
      10. References
    21. 18. Bay Area Blues: The Effect of the Housing Crisis
      1. Introduction
      2. How Did We Get the Data?
      3. Geocoding
      4. Data Checking
      5. Analysis
      6. The Influence of Inflation
      7. The Rich Get Richer and the Poor Get Poorer
      8. Geographic Differences
      9. Census Information
      10. Exploring San Francisco
      11. Conclusion
      12. References
    22. 19. Beautiful Political Data
      1. Example 1: Redistricting and Partisan Bias
      2. Example 2: Time Series of Estimates
      3. Example 3: Age and Voting
      4. Example 4: Public Opinion and Senate Voting on Supreme Court Nominees
      5. Example 5: Localized Partisanship in Pennsylvania
      6. Conclusion
      7. References
    23. 20. Connecting Data
      1. What Public Data Is There, Really?
      2. The Possibilities of Connected Data
      3. Within Companies
      4. Impediments to Connecting Data
      5. Possible Solutions
      6. Conclusion
    24. A. Contributors
    25. Index
    26. About the Authors
    27. COLOPHON
    28. SPECIAL OFFER: Upgrade this ebook with O’Reilly

Impediments to Connecting Data

Hopefully you're starting to be convinced that there are huge advantages to being able to easily integrate data from many different sources. But there are a few different reasons people aren't doing it already….

The Representation Problem

Perhaps the most basic problem with attempting to connect data sets is the fact that most data is stored in very inflexible structures. First of all, a surprising amount of important data in science and business is also kept in Excel spreadsheets, which are stored locally on people's computers, inaccessible to others and also not designed for integration anyway.

Even in companies where databases are made accessible, data is classically stored in relational databases, most of which have predefined schemas to fit the data that was initially believed to be important. Figure 20-2 shows a simple example of a relational schema for restaurant data. This is excellent for large, predictable data sets because relational databases have excellent performance when well configured, but presents problems when the application requires new kinds of data, new fields, or new relationships to be added frequently.

A relational schema for restaurant data.

Figure 20-2. A relational schema for restaurant data.

I've seen people solve this problem in a number of ways, but two really stand out, mostly because they're opposite ends of a spectrum. The traditional approach is to continually ...

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