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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

Spelling Correction

Our final task is spelling correction: given a typed word, w, determine what word c was most likely intended. For example, if w is "acomodation", c should be "accommodation". (If w is "the", then c too should be "the".)

Following the standard methodology, we want to choose the c that maximizes P(c | w). But defining this probability is not straightforward. Consider w = "thew". One candidate c is "the"—it's the most common word, and we can imagine the typist's finger slipping off the "e" key and hitting the "w". Another candidate is "thaw"—a fairly common word (although 30,000 times less frequent than "the"), and it is common to substitute one vowel for another. Other candidates include "thew" itself (an obscure term for muscle or sinew), "threw", and "Thwe", a family name. Which should we choose? It seems that we are conflating two factors: how probable is c on its own, and how likely is it that w could be a typo for c, or a mispronunciation, or some other kind of misspelling. One might think we will have to combine these factors in some ad hoc fashion, but it turns out that there is a mathematical formula, Bayes's theorem, that tells us precisely how to combine them to find the best candidate:

argmaxcP(c | w) = argmaxcP(w | c) P(c)

Here P(c), the probability that c is the intended word, is called the language model, and P(w | c ), the probability that an author would type w when c is intended, is called the error model or noisy channel model. (The idea is that ...

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