Beautiful data : 数据之美 :[优雅的数据解决方案背后的故事] /

　　PREFACE
　　1 SEEING YOUR LIFE IN DATA
　　 Personal Environmental Impact Report (PEIR)
　　 your.flowlngdata (YFD)
　　 Personal Data Collection
　　 Data Storage
　　 Data Processing
　　 Data Visualization
　　 The Point
　　 How to Participate
　　2 THE BEAUTIFUL PEOPLE: KEEPING USERS IN MIND WHEN DESIGNING DATA COLLECTION METHODS
　　 Introduction: User Empathy Is the New Black
　　 The Project: Surveying Customers About a
　　 New Luxury Product
　　 Specific Challenges to Data Collection
　　 Designing Our Solution
　　 Results and Reflection
　　3 EMBEDDED IMAGE DATA PROCESSING ON MARS
　　 Abstract
　　 introduction
　　 Some Background
　　 To Pack or Not to Pack
　　 The Three Tasks
　　 Slotting the Images
　　 Passing the Image: Communication Among the Three Tasks
　　 Getting the Picture: Image Download and Processing
　　 Image Compression
　　 Downlink, or, it's All Downhill from Here
　　 Conclusion
　　4.CLOUD STORAGE DESIGN IN A PNUTSHELL
　　 Introduction
　　 Updating Data
　　 Complex Queries
　　 Comparison with Other Systems
　　 Conclusion
　　5 INFORMATION PLATFORMS AND THE RISE OF THE DATA SCl ENTIST
　　 Libraries and Brains
　　 Facebook Becomes Self-Aware
　　 A Business Intelligence System
　　 The Death and Rebirth of a Data Warehouse
　　 Beyond the Data Warehouse
　　 The Cheetah and the Elephant
　　 The Unreasonable Effectiveness of Data
　　 New Tools and Applied Research
　　 MAD Skills and Cosmos
　　 Information Platforms As Dataspaces
　　 The Data Scientist
　　 Conclusion
　　6 THE GEOGRAPHIC BEAUTY OF A PHOTOGRAPHIC ARCHIVE
　　 Beauty in Data: Geo~raph
　　 Visualization, Beauty, and Treemaps
　　 A Geo2raphic Perspective on Geo~raph Term Use
　　 Beauty in Discovery
　　 Reflection and Conclusion
　　7 DATA FINDS DATA
　　 Introduction
　　 The Benefits of Just-in-Time Discovery
　　 Corruption at the Roulette Wheel
　　 Enterprise Discoverability
　　 Federated Search Ain't All That
　　 Directories: Priceless
　　 Relevance: What Matters and to Whom?
　　 Components and Special Considerations
　　 Privacy Considerations
　　 Conclusion
　　8 PORTABLE DATA IN REAL TIME
　　 Introduction
　　 The State of the Art
　　 Social Data Normalization
　　 Conclusion: Mediation via Gnip
　　9 SURFACING THE DEEP WEB
　　 What Is the Deep Web?
　　 Alternatives to Offering Deep-Web Access
　　 Conclusion and Future Work
　　10 BUILDING RADIOHEAD'S HOUSE OF CARDS
　　 How It All Started
　　 The Data Capture Equipment
　　 The Advantages of Two Data Capture Systems
　　 The Data
　　 Capturing the Data, aka "The Shoot"
　　 Processing the Data
　　 Post-Processing the Data
　　 Launching the Video
　　 Conclusion
　　11 VISUALIZING URBAN DATA
　　 Introduction
　　 Background
　　 Cracking the Nut
　　 Making It Public
　　 Revisiting
　　 Conclusion
　　12 THE DESIGN OF SENSE.US
　　 Visualization and Social Data Analysis
　　 Data
　　 Visualization
　　 Collaboration
　　 Voyagers and Voyeurs
　　 Conclusion
　　13 WHAT DATA DOESN'T DO
　　 When Doesn't Data Drive?
　　 Conclusion
　　14 NATURAL LANGUAGE CORPUS DATA
　　 Word Segmentation
　　 Secret Codes
　　 Spelling Correction
　　 Other Tasks
　　 Discussion and Conclusion
　　15 LIFE IN DATA: THE STORY OF DNA
　　 DNA As a Data Store
　　 DNA As a Data Source
　　 Fighting the Data Deluge
　　 The Future of DNA
　　16 BEAUTIFYING DATA IN THE REAL WORLD
　　 The Problem with Real Data
　　 Providing the Raw Data Back to the Notebook
　　 Validating Crowdsourced Data
　　 Representing the Data Online
　　 Closing the Loop: Visualizations to Suggest
　　 New Experiments
　　 Building a Data Web from Open Data and Free Services
　　17 SUPERFICIAL DATA ANALYSIS: EXPLORING MILLIONS OF SOCIAL STEREOTYPES
　　 Introduction
　　 Preprocessing the Data
　　 Exploring the Data
　　 Age, Attractiveness, and Gender
　　 Looking at Tags
　　 Which Words Are Gendered?
　　 Clustering
　　 Conclusion
　　18 BAY AREA BLUES: THE EFFECT OF THE HOUSING CRISIS
　　 Introduction
　　 How Did We Get the Data?
　　 Geocoding
　　 Data Checking
　　 Analysis
　　 The Influence of Inflation
　　 The Rich Get Richer and the Poor Get Poorer
　　 Geographic Differences
　　 Census Information
　　 Exploring San Francisco
　　 Conclusion
　　19 BEAUTIFUL POLITICAL DATA
　　 by Andrew Gelman, Jonathan P. Kastellec,
　　 and Yair Ghitza
　　 Example 1: Redistricting and Partisan Bias
　　 Example 2: Time Series of Estimates
　　 Example 3: Age and Voting
　　 Example 4: Public Opinion and Senate Voting on
　　 Supreme Court Nominees
　　 Example 5: Localized Partisanship in Pennsylvania
　　 Conclusion
　　20 CONNECTING DATA
　　 What Public Data Is There, Really?
　　 The Possibilities of Connected Data
　　 Within Companies
　　 Impediments to Connecting Data
　　 Possible Solutions
　　 Conclusion
　　CONTRIBUTORS
　　INDEX