Beautiful Data : The Stories Behind Elegant Data Solutions (Original)

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Beautiful Data : The Stories Behind Elegant Data Solutions (Original)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 364 p.
  • 言語 ENG,ENG
  • 商品コード 9780596157111
  • DDC分類 005.74

Full Description


In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging - and beautiful - working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With "Beautiful Data", you will: explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web; learn how to visualize trends in urban crime, using maps and data mashups; discover the challenges of designing a data processing system that works within the constraints of space travel; also learn how crowdsourcing and transparency have combined to advance the state of drug research; and, understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data. Learn about the massive infrastructure required to create, capture, and process DNA data. That's only small sample of what you'll find in "Beautiful Data". For anyone who handles data, this is a truly fascinating book. Contributors include: Nathan Yau; Jonathan Follett and Matt Holm; J.M.Hughes; Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava; Jeff Hammerbacher; Jason Dykes and Jo Wood; Jeff Jonas and Lisa Sokol; Jud Valeski; Alon Halevy and Jayant Madhavan; Aaron Koblin and Valdean Klump; Michal Migurski; Jeff Heer; Coco Krumme; Peter Norvig; Matt Wood and Ben Blackburne; Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen; Lukas Biewald and Brendan O'Connor; Hadley Wickham, Deborah Swayne, and David Poole; Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza; and, Toby Segaran.

Table of Contents

Preface                                            xi
Seeing Your Life in Data 1 (16)
Nathan Yau
Personal Environmental Impact Report 2 (1)
(PEIR)
your. flowingdata (YFD) 3 (1)
Personal Data Collection 3 (2)
Data Storage 5 (1)
Data Processing 6 (1)
Data visualization 7 (7)
The Point 14 (1)
How to Participate 15 (2)
The Beautiful People: Keeping Users in Mind 17 (18)
When Designing Data Collection Methods
Jonathan Follett
Matthew Holm
Introduction: User Empathy Is the New 17 (2)
Black
The Project: Surveying Customers About a 19 (1)
New Luxury Product
Specific Challenges to Data Collection 19 (2)
Designing Our Solution 21 (10)
Results and Reflection 31 (4)
Embedded Image Data Processing on Mars 35 (20)
J. M. Hughes
Abstract 35 (1)
Introduction 35 (2)
Some Background 37 (3)
To Pack or Not to Pack 40 (2)
The Three Tasks 42 (1)
Slotting the Images 43 (3)
Passing the Image: communication Among 46 (2)
the three Tasks
Getting the Picture: Image Download and 48 (2)
Processing
Image Compression 50 (2)
Downlink, or, It's All Downhill from Here 52 (1)
Conclusion 52 (3)
Cloud Storage Design in a Pnutshell 55 (18)
Brian F. Cooper
Raghu Ramakrishanan
Utkarsh Srivastava
Introduction 55 (2)
Updating Data 57 (7)
Complex Queries 64 (4)
Comparison with Other Systems 68 (3)
Conclusion 71 (2)
Information Platforms and the Rise of the 73 (12)
Data Scientist
Jeff Hammerbacher
Libraries and Brains 73 (1)
Facebook Becomes Self-Aware 74 (1)
A Business Intelligence System 75 (2)
The Death and Rebirth of a Data Warehouse 77 (1)
Beyond the Data Warehouse 78 (1)
The Cheetah and the Elephant 79 (1)
The Unresonable Effectiveness of Data 80 (1)
New Tools and Applied Research 81 (1)
MAD Skills and Cosmos 82 (1)
Information Platforms As Dataspaces 83 (1)
The Data Scientist 83 (1)
Conclusion 84 (1)
The Geographic Beauty of a Photographic 85 (20)
Archive
Jason Dykes
Jo Wood
Beauty in Data: Geograph 86 (3)
Visualization, Beauty, and Treemaps 89 (2)
A Geographic Perspective on Geograph Term 91 (7)
Use
Beauty in Discovery 98 (3)
Reflection and Conclusion 101(4)
Data Finds Data 105(14)
Jeff Jonas
Lisa Sokol
Introduction 105(1)
The Benefits of Just-in-Time Discovery 106(1)
Corruption at the Roulette Wheel 107(4)
Enterprise Discoverability 111(1)
Federated Search Ain't All That 111(2)
Directories: Priceless 113(2)
Relevance: What Matters and to Whom? 115(1)
Components and Special Considerations 115(3)
Privacy Considerations 118(1)
Conclusion 118(1)
Portable Data in Real Time 119(14)
Jud Valeski
Introduction 119(1)
The State of the Art 120(8)
Social Data Normalization 128(3)
Conclusion: Mediation via Gnip 131(2)
Surfacing the Deep Web 133(16)
Alon Halevy
Jayant Madhaven
What Is the Deep Web? 133(2)
Alternatives to Offering Deep-Web Access 135(12)
Conclusion and Future Work 147(2)
Building Radiohead's House of Cards 149(18)
Aaron Koblin
Valdean Klump
How It All Started 149(1)
The Data Capture Equipment 150(4)
The Advantages of Two Data Capture Systems 154(1)
The Data 154(1)
Capturing the Data, aka ``The Shoot'' 155(5)
Processing the Data 160(1)
Post-Processing the Data 160(1)
Launching the Video 161(3)
Conclusion 164(3)
Visualizing Urban Data 167(16)
Michal Migurski
Introduction 167(1)
Background 168(1)
Cracking the Nut 169(5)
Making It Public 174(4)
Revisiting 178(3)
Conclusion 181(2)
The Design of Sense.Us 183(22)
Jeffrey Heer
Visualization and Social Data Analysis 184(2)
Data 186(2)
Visualization 188(6)
Collabortion 194(5)
Voyagers and Voyeurs 199(4)
Conclusion 203(2)
What Data Doesn't Do 205(14)
Coco Krumme
When Doesn't Data Drive? 208(9)
Conclusion 217(2)
Natural Language Corpus Data 219(24)
Peter Norvig
Word Segmentation 221(7)
Secret Codes 228(6)
Spelling Correction 234(5)
Other Tasks 239(1)
Discussion and Conclusion 240(3)
Life In Data: The Story of DNA 243(16)
Matt Wood
Ben Blackburne
DNA As a Data Store 243(7)
DNA As a Data Source 250(3)
Fighting the Data Deluge 253(4)
The Future of DNA 257(2)
Beautifying Data in the Real World 259(20)
Jean-Claude Bradley
Rajarshi Guha
Andrew Lang
Pierre Lindenbaum
Cameron Neylon
Antony Williams
Egon Willighagen
The Problem with Real Data 259(1)
Providing the Raw Data Back to the 260(2)
Notebook
Validating Crowdsoured Data 262(1)
Representing the Data Online 263(8)
Closing the Loop: Visualizations to 271(3)
Suggest New Experiments
Building a Data Web from Open Data and 274(5)
Free Services
Superficial Data Analysis: Exploring 279(24)
Millions of Social Stereotypes
Brendan O'Connor
Lukas Biewald
Introduction 279(1)
Preprocessing the Data 280(2)
Exploring the Data 282(3)
Age, Attractiveness, and Gender 285(5)
Looking at Tags 290(4)
Which Words Are Gendered? 294(1)
Clustering 295(5)
Conclusion 300(3)
Bay Area Blues: The Effect of The Housing 303(20)
Crisis
Hadley Wickham
Deborah F. Swayne
David Poole
Introduction 303(1)
How Did We Get the Data? 304(1)
Geocoding 305(1)
Data Checking 305(1)
Analysis 306(1)
The Influence of Inflation 307(1)
The Rich Get Richer and the Poor Get 308(3)
Poorer
Geographic Differences 311(3)
Census Information 314(4)
Expolring San Francisco 318(1)
Conclusion 319(4)
Beautiful Political Data 323(12)
Andrew Gelman
Jonathan P. Kastellec
Yair Ghitza
Redistricting and Partisan Bias 324(2)
Time Series of Estimates 326(2)
Age and Voting 328(1)
Public Opinion and Senate Voting on 328(2)
Supreme Court Nominees
Localized Partisanship in Pennsylvania 330(2)
Conclusion 332(3)
Connecting Data 335(14)
Toby Segaran
What Public Data Is There, Really? 336(1)
The Possibilities of Connected Data 337(1)
Within Companies 338(1)
Impediments to Connecting Data 339(4)
Possible Solutions 343(5)
Conclusion 348(1)
Contributors 349(8)
Index 357