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Lifelogging - A long term data analytics challenge

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A talk delivered at the DBTA workshop on Lifelogging and Long-term Digital Preservation in Lugano, November 2015. The talk introduces lifelogging and the concept of the digital self. It highlights some potential advantages of lifelogging and suggests the technologies that we need to develop (or have developed) to realise these advantages. Finally it concludes with some insights based on my nine years of practitioner experience.

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Lifelogging - A long term data analytics challenge

  1. 1. LIFELOGGING A LONG-TERM DATA ANALYTICS CHALLENGE Cathal Gurrin cathal@gmail.com & @cathal Dublin City University, Ireland
 University ofTromsø, Norway 06 November 2015 - DBTA Workshop - Lugano
  2. 2. We generate huge amounts of personal data. The next step is lifelogging. In an era of Lifelogging, you will log all life experience into your digital self.
  3. 3. DIGITAL SELF An rich archive of data about the individual that captures the totality of life
 experience in a private, 
 non-forgetting archive. An lifelog archive that should provide ubiquitous support. Digital Self Lifelog
  4. 4. Why should we care? This is happening now with devices on the market already.
  5. 5. Dr Cathal Gurrin Faculty: DCU & UiT Scientist: Insight Centre for Data Analytics Lifelogger: It is what I do… A visual lifelog for 9+ years 70+ academic publications and one book in the area. Curiosity-driven scientist. I started this work without knowing where it would lead to.
  6. 6. INSIGHT CENTRE FOR DATA ANALYTICS 20/10/14 Slide 5 Healthier, safer and more productive world using data analytics.
  7. 7. AGENDA What is lifelogging? Vision for lifelogging How to make lifelog applications Progress made thus far Future expectations
  8. 8. WHAT WHAT -VISION - HOW - PROGRESS - FUTURE
  9. 9. Lifelogging refers to the process of storing data concerning the totality of life experience for future use… An data-driven extension of our memory and our cognitive abilities. It gets more valuable as it gets bigger. cars (legs)
 glasses (eyes)
 hearing aids (ears)
 pacemakers (hearts) lifelog (memory)
  10. 10. We will soon be able to capture a digital trace of the totality of life experience in digital archives
  11. 11. Data such as this…
  12. 12. 10 Years of location log, with millions of GPS points 2 Years heartbeats, with GSR and activity. Communicati ons 1 Year 
 of computer interactions (mouse, keyboard, email, WWW) 9 Years of lifelog, since 2006 16 Million images of what I see for 9+ years Grows at about 1TB per year. You can’t retrospectively gather and you don’t know what will be important later. Secure Personal Lifelog 1000+ Docs
 all I read and write and listen to My Digital Self (2006 - now)
  13. 13. Activities & Health
  14. 14. Everything I see
  15. 15. Everything I read
 or write on computer
 or digital paper Removed for privacy
  16. 16. VISION WHAT - VISION - HOW - PROGRESS - FUTURE From the point of view of the individual
  17. 17. Digital Self Life Experience A digital lifelog of all life experience… activities, experiences, thoughts, emotions… huge volumes of data that last a lifetime and beyond.
  18. 18. Focus on supporting knowledge acquisition and learning in the early years. 1. Knowledge Support From education to the workplace, providing information and insights to assist productivity and fitness. 2. Productivity Into old age, providing support for cognition and health to maintain independence and activity. 3. Health CHILD ADULT ELDERLY The lifelog will be a permanent companion assisting you throughout life. Constantly growing in size.
  19. 19. Quantified Self Enhanced Access to Knowledge Learning Support Performance Enhancement Personal Insights Data-driven Health Population-wide Analytics Healthcare Enhancement Enhancing Human Memory Assistive Technologies Synergy, not Substitution Assisted Memory Better Interactions Rich Sharing and Reminiscing Data Partners/Carers Social Enhancement Why?To provide knowledge to empower Quantified-Self Memory
  20. 20. Quantified-Self Analysis Self-discovery Reflect Calendar integration & Context Remind Sousveillance. Protection of me and bystanders Protect Find an item from the digital self Validate a memory Contextual support Search Find & share items Social applications Reminisce Digital Agents acting on our behalf, during life and after Represent My interest is in memory applications. So we can identify many ways that a lifelog / digital self can assist human memory.
  21. 21. Search www.loggerman.org
  22. 22. Quantified-Self Analysis Self-discovery Reflect Visual Reflection Software - EyeAware
  23. 23. Sousveillance. Protection of me and bystanders Protect
  24. 24. HOW WHAT -VISION - HOW - PROGRESS - FUTURE
  25. 25. We experience
 the world through our
 senses… add in our
 thoughts and emotions Considering humans…
  26. 26. Ideally we need a sensor that can record everything you see, hear and experience…
  27. 27. In 2015, lifelogging
 can generate
 thousands of
 images per day, hours of audio and tens of thousands of
 sensor readings
 per day. Images, audio, locations, movements,, heartbeats, stress, EEG, data interactions, communications, learnings, activities…
  28. 28. But.. all this data is meaningless without software to organise it… An organisation you trust to maintain it. And it to be always accessible.
  29. 29. There are a lot of technical challenges to be solved before a functioning lifelogging application is operational. This is an enormous research challenge. Segmentation Find the unit of retrieval for many use- cases… there is no one correct unit Enrichment Automatically turn raw sensor data into meaningful information Search Engine To index the data Access Support Supporting long-term use with many use cases Assuming you have Rich Sensing
  30. 30. Many events into one logical semantic unit Experience 4 5 A logical concatenation of moments into a semantic unit (about 30 per day) Event 3 Multi-modal raw data. Items 1 A short snapshot of activity Moments 2 A summary, a reflection or an analytical aggregation of data. Abstraction Segment into Units of Retrieval Depends on the Information Need - which changes over time
  31. 31. An Event Segmentation Model
  32. 32. Another Event Segmentation Model
  33. 33. Raw$ Sensors$ What$doing$ What$ Environment$ Movement$ • Ac8vity$ • Energy$ Where$Who$is$there$ When$$ Why$ “Shopping for a coat last
 Tuesday in Dublin” Enrichment to add semantic value. Necessary for most use-cases.
  34. 34. Develop Appropriate Search Engines Ubiquitous Search Contextual Search Push & Pull Interaction Browsing and Summarisation
  35. 35. And Understand the Use-Cases
  36. 36. PROGRESS WHAT -VISION - HOW - PROGRESS - FUTURE
  37. 37. 1928
 Bucky- Fuller 1980s
 Steve Mann 2004
 Williams (Sensecam) 2015
 First Gen Tech. 2006+
 Memory Studies 2004
 G. Bell
 (MyLifeBits) 1946
 Vannevar Bush
  38. 38. 1928
 Bucky- Fuller Dymaxion Chronofile A very accurate physical record of a human (every 15 minutes from 1920-1983)
  39. 39. 1946
 Vannevar Bush MEMEX A lifelog and hypermedia proposal
  40. 40. 1980s
 Steve Mann Built a version of Google Glass 25 years ago.
  41. 41. 2004
 G. Bell
 (MyLifeBits) Gordon Bell’s MyLifeBits, a first true lifelog.
  42. 42. 2004
 Williams (Sensecam) SenseCam - up to 5,000 images per day
  43. 43. 2006+
 Memory Studies Berry, Hodges, Moulin, Doherty First studies into the memory-support potential
  44. 44. 2015
 First Gen Tech. Narrative Clip camera… 2,000 photos per day.
  45. 45. And a lot of early applications, by ourselves and others Early insights into the nature of lifelog search engines
  46. 46. An Event Segmentation Model
  47. 47. Aizawa’s Foodlog
  48. 48. Reporter App Reporter &The Annual Reports Nick Feltron
  49. 49. SIMPLE ANALYTICS QUANTIFIED SELF
  50. 50. DEEP ANALYTICS UNDERSTAND WHATTHE USER EXPERIENCES Find anything you have seen before. Objects, people, brands, environments, etc…
  51. 51. EYEAWARE PLATFORM
  52. 52. COLOUR OF LIFE
  53. 53. ! ! SOCIETAL ANALYTICS
  54. 54. FUTURE WHAT -VISION - HOW - PROGRESS - FUTURE
  55. 55. Lifelogging will create a whole new set of
 opportunities & challenges for
 industry and society… but This is private data and it is not curated Most of it will never be seen and the lifelogger will forget what is there Data security is vital with huge consequences Data outlives the person The digital self must be self-organising Who can access the data (now and post-life)?
  56. 56. Many Opportunities New Sensors New Search Engines New Scientific Challenges Opportunities for Assistive Technologies But Little Understanding of the Use-Cases
  57. 57. Privacy is a big issue… Throughout life and afterwards.
  58. 58. The Privacy Paradox Wearer or Bystander… who looses more privacy?
  59. 59. Some Thoughts for Digital Preservation I don’t know what my digital self actually stores. The data is incomplete and inaccurate I have no time to curate it and I can’t easily manage it. It needs to be self-curating and self-organising The data will outlive the individual - Someday somebody will look at it Data becomes the truth
  60. 60. THANKYOU @cathal & cathal@gmail.com http://about.me/cgurrin/ Any Questions? Thanks to the DCU team and our collaborators! DCU 2015 L i f e l o g g i n g - P e r s o n a l B i g Data… from the F o u n d a t i o n a n d Trends in Information Retrieval series. Free d o w n l o a d , a s k Google. NTCIR-12 Lifelog task First comparative evaluation campaign for lifelog data and search

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