The Data-Driven World
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The Data-Driven World
- 1. The Data-Driven World Kaur Alasoo
- 2. Kaur Alasoo • Computer Science, University of Tartu (2007-2010) • Intern at European Molecular Biology Laboratory (April - August 2010) • Systems Biology at Aalto University (2010 - 2012)
- 3. Outline • Where are we now? • Where are we heading to? • How to get there?
- 4. Where are we now?
- 5. Where are we now? 1 Science: Culturomics 2 Society: OkCupid 3 Personal life: Tracking sites
- 7. To keep up with the lexicon, dictionaries are to supplant them (Fig. 2E and fig. S5). High- significant driver of re updated regularly (13). We examined how well frequency irregulars, which are more readily 200 years. The regulariz Culturomics: Using Google these changes corresponded with changes in ac- tual usage by studying the 2077 1-gram headwords remembered, hold their ground better. For in- stance, we found “found” (frequency: 5 × 10−4) and spilt originated in forms still cling to life i Books to analyze culture added to AHD4 in 2000. The overall frequency of these words, such as “buckyball” and “netiquette”, has soared since 1950: Two-thirds exhibited recent 200,000 times more often than we finded “finded.” In contrast, “dwelt” (frequency: 1 × 10−5) dwelt in our data only 60 times as often as “dwelled” E and F). But the -t irre England too. Each year Cambridge adopts “bur Fig. 1. Culturomic analy- A B ses study millions of books at once. (A) Top row: Au- thors have been writing for millennia; ~129 mil- lion book editions have been published since the 129 million books advent of the printing press published (upper left). Second row: Libraries and publishing houses provide books to Google for scanning (mid- dle left). Over 15 million 15 million books C books have been digitized. scanned Third row: Each book is associated with metadata. Five million books are cho- sen for computational anal- ysis (bottom left). Bottom 5 million books row: A culturomic time line analyzed Frequency of the shows the frequency of word "apple" “apple” in English books over time (1800–2000). Year (B) Usage frequency of
- 8. Fame depends on profession F Median frequency 3” 1871 (gray lines; median, thick dark gray line). Five examples are highlighted.
- 9. birth date and (Fig. 3E). The age of peak celebrity has been con- similar (7) (fig. S 1800 to 1950, sistent over time: about 75 years after birth. But famous than eve of the 50 most the other parameters have been changing (fig. S8). more rapidly than Tracking censorship A B Frequency Frequency wikipedia.org
- 10. D B Frequency (fig. S8). more rapidly than ever. www.sciencemag.org on April 21, 2011
- 11. en su History of science ar M F w re “R ex m en fi id la
- 12. Feminism C D
- 13. 193 and Gender equality dis ter D lie ske wa far sup enr suc arc Ma
- 16. OkCupid: data mining to analyze dating
- 17. men want to date most attractive women
- 18. most men are below average
- 22. Goodreads tracks all books you have ever read
- 23. Goodreads tracks all books you have ever read
- 24. Goodreads tracks all books you have ever read
- 26. Travel planning Visited places Read books Flight log Exercise tracking Conversation log Activity tracking
- 27. Fitbit: tracking physical activity
- 28. Where are we heading to?
- 29. Where are we heading to? 1 Science: Other fields 2 Society: Data integration 3 Personal life: Data integration
- 31. Studying art history with image processing?
- 32. Analyzing literature using text mining? http://www.textually.org/textually/archives/2009/02/022619.htm
- 33. Co-mentioning of people When Arno and his father reached school, the classes had already begun. Kevade by Oska Luts
- 34. Co-mentioning of people When Arno and his father reached school, the classes had already begun. Kevade by Oska Luts
- 35. Co-mentioning of terms When Arno and his father reached school, the classes had already begun. Kevade by Oska Luts
- 39. 3 Personal Life: Data Integration
- 40. Almost complete log of personal life
- 41. Travel planning Visited places Read books Flight log Exercise tracking Conversation log Activity tracking
- 42. Data integration Public transport log Purchase history
- 43. How to get there?
- 44. Lack of data won’t be the problem http://www.sand.com/wp-content/uploads/2011/04/binary_data.jpg
- 45. Data integration and analysis skills are important http://www.keralaevents.com/eventphotos/799/Dataanalysis_1.jpg
- 46. Privacy has to be preserved http://www.geekologie.com/2008/04/warmth_and_privacy_while_using.php
- 47. Privacy-preserving data mining http://sharemind.cyber.ee/
- 48. Hard part is to come up with good questions http://www.hhmi.org/bulletin/dec2005/chronicle/crosstalk.html
- 49. Conclusion
- 50. Conclusion • There are already many successful examples of data-rich applications. • More and more data will become available in many different fields. • Collecting data is easy. Difficulties lie in analyzing it and understanding what it means.
- 51. References • Quantitative Analysis of Culture Using Millions of Digitized Books, Jean-Baptiste Michel, et al. Science 331, 176 (2011) • OkTrends: Dating research from OkCupid http://blog.okcupid.com • The Data-Driven Life, Gary Wolf, The New York Times, http:// www.nytimes.com/2010/05/02/magazine/02self-measurement- t.html • The Quantified Self | self knowledge through numbers http://quantifiedself.com