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neilellis@cazcade.com
THANK YOU FOR BEING     my guinea pigs today
I’D LOVE YOUR FEEDBACK TOO
SERENDIPITYHow do I find what I am not looking for?
Serendipity: the occurrence and development of events by chance in a happy or beneficial way
HOW SERENDIPITY HELPS• Many new inventions occur because related information crosses conventional boundaries, leaving it’s...
Serendipity has lead to an incredible amount of discoveries.
A PRACTICAL EXERCISE  Please fill in the forms I handed out.
WHO NEEDS SERENDIPITY?•   B2B Sites - encourages businesses to find ways of collaborating they may    never have thought of...
THE THREE STEP PLAN
STEP 1: REMOVE ISOLATION
Science Books                 Art Books                   Cookery BooksSEMANTICALLY ISOLATED
User              Blog Posts  DocumentsCONTEXTUALLY ISOLATED
DocumentsCONTENT CONNECTED
UsersDocuments            Blog Posts                Documents                         Blog Posts  SOCIALLY CONNECTED
Documents Users           Blog PostsHIGHLY CONNECTED
GET CONNECTED•   Contextually isolated systems only show us information regarding a closed set of    data and activities.•...
OUR STORAGE SYSTEMSAFFECT HOW CONNECTED  WE MAKE THE WORLD
FILE BASED STORAGE SEES THEWORLD AS A SET OF NESTED COLLECTIONS OF ISOLATED        INFORMATION
LIKE FILING CABINETS
OR A WAREHOUSE
RELATIONAL DATABASES AS HIGHLY  ORGANISED COLLECTIONS OF        INFORMATION       WHICH INTERSECT
LIKE ENROLMENT
LIKE BANKING
OR AN OCD LARDER
AND GRAPH DATABASES ASDISORGANISED BUT HIGHLY INTERCONNECTED DATA
LIKE .....
The Human Brain
Ideas
People
The Internet
Data
AND POSSIBLY EVERYTHING!
RDMS VS GRAPH• Highly      connected systems can be modelled relatively easily on an RDMS, but adding new relationships cr...
STEP 2: USE MULTIPLE HOPS
User  User          DocumentsRECOMMEND A FRIEND
User             Documents YOU MIGHT ALSO LIKE
RDMS VS GRAPH• Multiple        hop queries are horrific under an RDBMS in both performance pitfalls and legibility of queri...
STEP 3: WEIGHT AND FILTER
WEIGHT & FILTER• Proximitystill matters, information should be closely  connected if not semantically or contextually rela...
RDMS VS GRAPH• RDMS cannot categorise relationships independently of the content for example ‘like’, ‘owns’, ‘has viewed’....
EXAMPLES
TEFLON FRYING PANS:SERENDIPITY IN ACTION
Marc Grégoire   Mme. GrégoireINVENTED BY MARC GREGOIRE  AT THE BEHEST OF HIS WIFE
Marc Grégoire    PTFEMARC USED PTFE ON HIS TACKLE
Mme. GrégoireHIS WIFE WANTED PANS THAT DIDN’T STICK
PTFESEMANTICALLY ISOLATED
Marc Grégoire   Mme. Grégoire    PTFECONTEXTUALLY ISOLATED
Marc Grégoire   Mme. Grégoire  PTFESOCIALLY CONNECTED
Marc Grégoire   Mme. GrégoirePTFEMULTIPLE HOPS
Mme. GrégoirePTFE       SERENDIPITY
Marc Grégoire   Mme. Grégoire     PTFEHIGHLY CONNECTED SYSTEM
RE-TWEET
RE-TWEETS• Re-tweets allow rapid dissemination of information beyond a limited social group, they cross semantic and conte...
HAVE YOU FILLED IN YOUR       FORMS?
WHAT SERENDIPITY ISN’T!• Random; random   combinations of information are just noise. putting teflon on a dolphin’s nose wo...
THREE STEPS TO SERENDIPITY• Remove     Isolation. Relationships are low cost and can be added to data at any point, so cre...
CODING SERENDIPITY       How can we add serendipity into our systems?• Information   must be able to travel freely between...
HOW NEO4J HELPS• Relationships              are created trivially at low cost at any time with no regards to semantic boun...
TAKE AWAY• Create   more relationships.• Let   information cross contextual and semantic boundaries.• Make    sure relevan...
@neilellisneilellis@cazcade.com
AUTOMATIC WEIGHT&FILTER• Sum    the ‘weight’ of each relationship traversed to the node.• Find   a random number between 0...
MANUAL WEIGHT&FILTER• Re-Tweeting        or forwarding.• Tell   a friend.• Like.• etc.
OTHER EXAMPLES•   Research papers are a semantically arranged collection of information and    therefore create semantic i...
Serendipity
Serendipity
Serendipity
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Serendipity

The importance of serendipity in collaborative software.

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Serendipity

  1. 1. neilellis@cazcade.com
  2. 2. THANK YOU FOR BEING my guinea pigs today
  3. 3. I’D LOVE YOUR FEEDBACK TOO
  4. 4. SERENDIPITYHow do I find what I am not looking for?
  5. 5. Serendipity: the occurrence and development of events by chance in a happy or beneficial way
  6. 6. HOW SERENDIPITY HELPS• Many new inventions occur because related information crosses conventional boundaries, leaving it’s ghetto.• Ourlives are made richer by discovering ideas and experiences outside our comfort zones and habitual patterns.• Serendipity accelerates information discovery by making new and unexpected connections.
  7. 7. Serendipity has lead to an incredible amount of discoveries.
  8. 8. A PRACTICAL EXERCISE Please fill in the forms I handed out.
  9. 9. WHO NEEDS SERENDIPITY?• B2B Sites - encourages businesses to find ways of collaborating they may never have thought of.• Social sites - let people discover new friends and new interests.• Collaborative software - find projects that could work together in unexpected ways.• Document management - find documents that help you look at your work in a different way?• Contact management - find new people who you could do business with that might not be in a narrowly defined field.
  10. 10. THE THREE STEP PLAN
  11. 11. STEP 1: REMOVE ISOLATION
  12. 12. Science Books Art Books Cookery BooksSEMANTICALLY ISOLATED
  13. 13. User Blog Posts DocumentsCONTEXTUALLY ISOLATED
  14. 14. DocumentsCONTENT CONNECTED
  15. 15. UsersDocuments Blog Posts Documents Blog Posts SOCIALLY CONNECTED
  16. 16. Documents Users Blog PostsHIGHLY CONNECTED
  17. 17. GET CONNECTED• Contextually isolated systems only show us information regarding a closed set of data and activities.• Semantically isolated systems only show us information which is similar to other information.• Content connected systems show us data that relates to each other which can crosses weakening contextual and semantic boundaries.• Socially connected systems show us information regarding our friends and their activities, weakening contextual and semantic boundaries.• Highly connected systems show us information with n-degrees of separation and multiple paths across contextual and semantic boundaries.
  18. 18. OUR STORAGE SYSTEMSAFFECT HOW CONNECTED WE MAKE THE WORLD
  19. 19. FILE BASED STORAGE SEES THEWORLD AS A SET OF NESTED COLLECTIONS OF ISOLATED INFORMATION
  20. 20. LIKE FILING CABINETS
  21. 21. OR A WAREHOUSE
  22. 22. RELATIONAL DATABASES AS HIGHLY ORGANISED COLLECTIONS OF INFORMATION WHICH INTERSECT
  23. 23. LIKE ENROLMENT
  24. 24. LIKE BANKING
  25. 25. OR AN OCD LARDER
  26. 26. AND GRAPH DATABASES ASDISORGANISED BUT HIGHLY INTERCONNECTED DATA
  27. 27. LIKE .....
  28. 28. The Human Brain
  29. 29. Ideas
  30. 30. People
  31. 31. The Internet
  32. 32. Data
  33. 33. AND POSSIBLY EVERYTHING!
  34. 34. RDMS VS GRAPH• Highly connected systems can be modelled relatively easily on an RDMS, but adding new relationships creates complexity and must be planned in advance.• Queryingis easier for semantically and contextually isolated models on an RDMS.• Querying is extremely messy (indeed!) for highly connected models.
  35. 35. STEP 2: USE MULTIPLE HOPS
  36. 36. User User DocumentsRECOMMEND A FRIEND
  37. 37. User Documents YOU MIGHT ALSO LIKE
  38. 38. RDMS VS GRAPH• Multiple hop queries are horrific under an RDBMS in both performance pitfalls and legibility of queries.• Graph databases love multiple hop logic and one can say thrive upon it. It’s much easier to find out related items through arbitrary degrees of separation and semantic barriers.
  39. 39. STEP 3: WEIGHT AND FILTER
  40. 40. WEIGHT & FILTER• Proximitystill matters, information should be closely connected if not semantically or contextually related.• Relevancy should relate to frequency.• Filtering can be done manually by users choosing what to recommend or pass on.• If possible use customer feedback to adjust weighting.
  41. 41. RDMS VS GRAPH• RDMS cannot categorise relationships independently of the content for example ‘like’, ‘owns’, ‘has viewed’.• RDMS cannot add meta-data to the relationship to help ranking of the relevancy.• Graph databases can do both these and can quickly calculate the cost of traversing to an item of content.
  42. 42. EXAMPLES
  43. 43. TEFLON FRYING PANS:SERENDIPITY IN ACTION
  44. 44. Marc Grégoire Mme. GrégoireINVENTED BY MARC GREGOIRE AT THE BEHEST OF HIS WIFE
  45. 45. Marc Grégoire PTFEMARC USED PTFE ON HIS TACKLE
  46. 46. Mme. GrégoireHIS WIFE WANTED PANS THAT DIDN’T STICK
  47. 47. PTFESEMANTICALLY ISOLATED
  48. 48. Marc Grégoire Mme. Grégoire PTFECONTEXTUALLY ISOLATED
  49. 49. Marc Grégoire Mme. Grégoire PTFESOCIALLY CONNECTED
  50. 50. Marc Grégoire Mme. GrégoirePTFEMULTIPLE HOPS
  51. 51. Mme. GrégoirePTFE SERENDIPITY
  52. 52. Marc Grégoire Mme. Grégoire PTFEHIGHLY CONNECTED SYSTEM
  53. 53. RE-TWEET
  54. 54. RE-TWEETS• Re-tweets allow rapid dissemination of information beyond a limited social group, they cross semantic and contextual boundaries.• Re-tweets can be (and are often) re-tweeted, allowing multiple hops.• Other Twitter users act as the filters, and we further weight by reputation.
  55. 55. HAVE YOU FILLED IN YOUR FORMS?
  56. 56. WHAT SERENDIPITY ISN’T!• Random; random combinations of information are just noise. putting teflon on a dolphin’s nose would not be a useful contribution to society. Don’t confuse unexpected with random!• Accidental; serendipitycomes from an attentive, and often intuitive mind receiving diverse information.• Luck; serendipity is a cognitive process that creates new connections between previously unrelated concepts and realises the value in them.
  57. 57. THREE STEPS TO SERENDIPITY• Remove Isolation. Relationships are low cost and can be added to data at any point, so create them and create as many as possible ignoring contextual or semantic boundaries.• UseMultiple Hops. Cross semantic and contextual boundaries when providing relevancy.• Weight and Filter. The value of the information found should relate to the route traversed. Allow users to manually pass on information to others.
  58. 58. CODING SERENDIPITY How can we add serendipity into our systems?• Information must be able to travel freely between users.• Information should be able to travel multiple levels of indirection with ease.• Information should have the maximum number of inter- connections across semantic boundaries.• Information relationships should be categorised and potentially contain meta-data required for weighting.
  59. 59. HOW NEO4J HELPS• Relationships are created trivially at low cost at any time with no regards to semantic boundaries.• Connected information over many hops can be retrieved quickly using Node#traverse or the Traversal framework.• Relationships can have both types and properties making weight and filter calculations easy.
  60. 60. TAKE AWAY• Create more relationships.• Let information cross contextual and semantic boundaries.• Make sure relevancy is probabilistic, not deterministic.• Serendipity is not accidental, random or lucky!• Themore heterogeneous and connected your data becomes, the more you should consider Neo4j.
  61. 61. @neilellisneilellis@cazcade.com
  62. 62. AUTOMATIC WEIGHT&FILTER• Sum the ‘weight’ of each relationship traversed to the node.• Find a random number between 0 and that weight.• Order the discovered nodes by this random value.• Choose the nodes with the nth lowest values.• Byusing random numbers we increase serendipity without sacrificing relevance.
  63. 63. MANUAL WEIGHT&FILTER• Re-Tweeting or forwarding.• Tell a friend.• Like.• etc.
  64. 64. OTHER EXAMPLES• Research papers are a semantically arranged collection of information and therefore create semantic isolated areas of information.• A lending library is another semantically isolated collection of information.• A project management website creates a contextually isolated set of information.• The internet is a highly connected disorganised information storage system - which leads to a fair amount of serendipity. How many interesting things have you ‘stumbled upon’ on the internet, but it still has a tendency to have semantic or contextual silos. There’s still a lot of room for improvement.

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