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Objective storytelling 141106

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The nature of storytelling has been evolving. Now it is becoming more data-based in many applications. This objective storytelling is closely tied to rise of big data.

Published in: Data & Analytics

Objective storytelling 141106

  1. 1. 1
  2. 2. • To live is to communicate! – All Human dealings are based on communica7on. Modern model of Communica0on
  3. 3. • Verbal • Non Verbal • Formal • Informal
  4. 4. • Informa7on Sharing • Knowledge Sharing • Emo7on Sharing • Comple7ng transac7ons
  5. 5. Even scien0fic papers.
  6. 6. • A story is a chain of events that begins at one place and ends at another. • A story is a selec0ve batch of informa0on. It selects details, arranges them, perhaps embellishes them. hEp://janefriedman.com/2011/09/27/what-­‐is-­‐a-­‐story/
  7. 7. • Storytelling is conveying of event experiences in – Words, – Images, – Audio – Mul7media • For – Entertainment – Educa7on – Cultural preserva7on – Moral values
  8. 8. • Imaginary – Mythology – Science Fic7on – Fic7on • Historical • Hybrid • Data based 8
  9. 9. Mythology shapes culture: Mythological stories become stronger than historical facts.
  10. 10. 12 Hans Rosling from Gapminder.org
  11. 11. • Capture • Storage • Selec7on/Filtering • Transmission • Rendering
  12. 12. Human memory Big Data
  13. 13. 16
  14. 14. 17
  15. 15. 18
  16. 16. Media • Marshal McLuhan in 1964. – A medium itself, not the content it carries, should be the focus of study. – A medium affects the society in which it plays a role not only by the content delivered over the medium, but also by the characteris@cs of the medium itself.
  17. 17. Media + + Digital technology allowed combining and using media leading to a major disrup0on.
  18. 18. 21 From Mary Meeker’s presenta@on
  19. 19. • Con7nuously growing warehouse of events. • Events related to Everything.
  20. 20. • In Real World. • Images, video, audio, all other sensors are just a means to detect and capture some aspect of the event.
  21. 21. • Prepare a database of all events. – Collect all experien0al and informa0onal data about each event. • Considering audience and the message, select important events. • Render Events – For each event decide what informa0on and experien0al data should be used. – Render all events and related data in the best format for highest quality of experience.
  22. 22. Inven0ng Events Selec0ng Events Rendering Events Imagine
  23. 23. Collec0ng Events Selec0ng Events Rendering Events Analyze Data
  24. 24. 30
  25. 25. 31
  26. 26. 32
  27. 27. • Why is video edi0ng so difficult? – Even aOer so many products? • How can we make it usable by consumers? • Is Video edi0ng a real problem? • Is it possible to reinvent video edi0ng given all the ‘big data’?
  28. 28. Face ID Lyn Russell Hillary Clinton Lyn Russell Clinton running for senate Speaking at rally Improving Medicare “Clinton spoke with reporters” “When She arrived” “Older citizens who have” 00:08:26:12 00:08:34:29 00:08:40:00 00:08:26:12 00:08:34:29 00:08:40:00 Speaker ID User Annotation Speech to Text Time Code Keyframes Video Time Hillary Clinton © Ramesh Jain 34
  29. 29. OTHER Ac7vity Loca7on Manual Audio Visual Walk Walk Walk Walk Walk Walk Home Drive Mee7ng Work Exercise Home Drive Home DriveM ee7ng WoMrke e7ng Work Exercise Exercise Event Track
  30. 30. 36
  31. 31. • Traveled a lot – and took about 15000 photos. • Want to share Story of Year 2014 in Photos. • Use about 15-­‐20 photos. • With – My professional friends – My family – My friends – My …
  32. 32. Your Personalized Visual Web
  33. 33. • Top 8 Apps in 2013 were Visual. • Selfies are becoming a communica7on tool. • Significantly more 7me spent on visual media. Source: hEp://digiday.com/plagorms/5-­‐charts-­‐growth-­‐visual-­‐web/
  34. 34. Events Loca7o n People
  35. 35. • Biographies and Auto-­‐biographies • Using it for sharing experiences • Predic0ng and controlling future
  36. 36. Un0l recently, you were either your memories or a folder.
  37. 37. Now
  38. 38. • Anecdotal • Diarizing data • Quan0fied Self Objec0ve Self
  39. 39. Health Profile Profess-­‐ ional Interest Social Perosna-­‐ lity • Sports • Books • Movies • Music • … • Family • Friends • Religion • Poli7cs • … • Type • Temper • Style • Appear-­‐ ence • … • Gene7cs • Life Style • Medicine • Known Ailments • … • Personal Info • Languages • Contact Info • Special Info • … • Current • Past • Exper7se • Leadership • …
  40. 40. • When did I get interested in Digital Storytelling? • When did my friendship with Tom start gekng sour? Why? • What causes me sleepless nights?
  41. 41. • Ge[ng objec0ve data • Storing data • Analyzing data • Privacy of data.
  42. 42. Ac0vi0es Media Communica0ons AGGREGATED PERSONAL ANALYTICS Sensors Cyber-­‐ Physical-­‐ Social Cloud
  43. 43. • Con7nuously growing warehouse of personal events. • Events related to Everything in personal life. • Private, Social, Professional, Health, Spiritual, …
  44. 44. • Personicle = Personal Chronicle • Personal Data warehouse • BeEer if automa7cally collected 56
  45. 45. • Con0nuous sensors crea0ng ‘health persona’. • Predic0ve techniques for preven0ve advice. • Quan0ta0ve measurements for be^er control.
  46. 46. • Understanding personal ac0ons in context. • Crea0ng Personal ‘Good Health Habits’ in own eco-­‐system. • Contextual persuasive pervasive feedback. • Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives.
  47. 47. • Red: “Severe asthma attack”. • Orange: High activity level. • Exercise triggers asthma! Personicle Ac7vity Stream t1 t2 t3 t4 t5
  48. 48. • Blue: Shortness of breath. The lighter the color, the worse the symptom. • Green: Low air pollution. • Red to Purple: High and very high air pollution • Exposure to polluted air is a risk factor of asthma attack within X hour? Personicle case Environmental factors
  49. 49. • Storytelling is older profession than supposedly oldest profession. • We can detect events using many sources of data; and we may use those in stories. • Stories derived from Personicles represent Objec0ve Self. – Objec0ve self helps individuals in many ways. – By u0lizing objec0ve self data for society, we can build be^er societal models for solving challenging problems.
  50. 50. For ques7ons: jain@ics.uci.edu

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