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ForgetIT: human memory inspired Information Model
- 1. ForgetIT: human memory inspired Information Model PERICLES - 27 April 2016 Walter Allasia allasia@eurix.it - walter.allasia@unito.it http://forgetit-project.eu/
- 2. Agenda ● How human memory works ○ Connecting the memory operation to its computer equivalent ○ Fundamental similarities between human memory and digital preservation: remember=retrieving? ● Main components of the conceptualized Information Model ● Implementing the “preservation value”
- 3. How memory works What’s the value of ?
- 4. How memory works What’s the value of ? Answer: 3.14
- 5. How memory works Picture taken from: https://www.trainingindustry.com/wiki/entries/forgetting-curve. aspx
- 6. How memory works What’s the value of ? Answer: 3.1415
- 7. How memory works Picture taken from: http://avidcollegeready.org/college-career-readiness/2011/1/19/why-do-i-have-to-take-notes-the-brain-note-connection. html
- 8. How memory works Picture taken from: http://avidcollegeready.org/college-career-readiness/2011/1/19/why-do-i-have-to-take-notes-the-brain-note-connection. html Re-Learning!
- 9. How memory works cue: Cue specific encoding Math stuff Geometry stuff Circle stuff Spreading Activation Model Constants 3.1415 This is an example
- 10. How memory works Picture taken from: http://www.kpetersen.com/rustic-oak-restaurant-tables. htm What can you eat at a restaurant you have never been to before?
- 11. How memory works Picture taken from: http://www.kpetersen.com/rustic-oak-restaurant-tables. htm What can you eat at a restaurant you have never been to before? Menu on the tables provides the list of available food
- 12. How memory works Picture taken from: http://www.kpetersen.com/rustic-oak-restaurant-tables. htm What can you eat at a restaurant you have never been to before? Menu on the tables provides the list of available food Pre-existing knowledge is filling the gaps
- 13. How memory works Pre-existing knowledge Knowledge Base Semantic Memory
- 16. Working memory Working Memory Procedural MemorySemantic Memory Episodic Memory Personal perspective
- 17. Working memory Picture taken from: http://www.ou.edu/deptcomm/dodjcc/groups/02b1/02b1litreview.htm Organizational perspective
- 19. ForgetIT approach Personal Contents Organizational Contents Preserve or Forget (PoF) Middleware Digital Preservation System (DPS)
- 20. The PoF Preserve-or-Forget Architecture
- 21. The PoF Preserve-or-Forget Architecture Organizational perspective Personal perspective C M I S
- 23. Processing flow: from Episodic Element to Content Episodic Element (date,place, people,context,..) sensor inputs Initial Perception
- 24. Processing flow: from Episodic Element to Content take pictures Episodic Element sensor inputs record audio other devices ... annotations, tags, ... Initial Perception
- 25. Processing flow: from Episodic Element to Content take pictures Device Metadata (Date, GPS, FoV, Light,...) Episodic Element sensor inputs record audio other devices ... annotations, tags, ... Initial Perception Specific Cue encoding Extract cue (hook)
- 26. Processing flow: from Episodic Element to Content take pictures Device Metadata (Date, GPS, FoV, Light,...) Episodic Element sensor inputs record audio other devices ... annotations, tags, ... Initial Perception Specific Cue encoding Extract cue (hook) Content Processing
- 27. Processing flow: updating the knowledge base BLOB FeaturesConcepts Ancillary... Context
- 28. BLOB FeaturesConcepts Ancillary... Graph Structures cueconcept1 concept2 concept3 concept 4 W1,3 W1,4 W2,4 W1,2 Context Knowledge Base and Currently Activated Associations Processing flow: updating the knowledge base
- 29. BLOB FeaturesConcepts Ancillary... Relational StructuresGraph Structures cueconcept1 concept2 concept3 concept 4 W1,3 W1,4 W2,4 W1,2 Context Clusters Others... Knowledge Base and Currently Activated Associations Processing flow: updating the knowledge base
- 30. Graph of concepts in images city park cue/hookc1
- 31. Graph of concepts in images city park cue/hook trees outdoor vegetation building urban_park plant sky daytime_outdoor suburban blue green garden c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 a9,11
- 32. Building the knowledge base Score array Sample set provided by CERTH 11270 images 346 concepts
- 33. Building the knowledge base Score array Correlation Matrix Sample set provided by CERTH 11270 images 346 concepts
- 34. Building the knowledge base Score array Correlation Matrix Weighted Adjacency Matrix Sample set provided by CERTH 11270 images 346 concepts
- 35. The “Network of concepts”
- 36. Statistical analysis of network The Semantic Memory represented with this approach can be considered a “Complex Network”: - clustering coeff: 0.21 - avg path length: 2.8 - asymmetric, fat-tailed P(s) Size of the giant component (Largest Connected Component) in function of the fraction of nodes removed
- 38. User Perspective
- 39. User Perspective
- 42. Computing the Preservation Value Contents to be preserved PoF Middleware Distributed / Cloud DPS From texts: extract context, main topics, etc... From images: extract concept, main topics, features, etc… Personal perspective: PIMO+PoF processors Organizational perspective: Typo3+PoF processors
- 43. Computing the Preservation Value Contents to be preserved PoF Middleware Distributed / Cloud DPS From texts: extract context, main topics, etc... From images: extract concept, main topics, features, etc… Personal perspective: PIMO+PoF processors Organizational perspective: Typo3+PoF processors If topics or specific features are similar to something already processed, the related contents are suitable to be more important for the user/organization
- 44. Computing the Preservation Value Contents to be preserved PoF Middleware Distributed / Cloud DPS Time T0 Retention % (human) = preservation value (IS) Time T1
- 45. Thanks for your attention Further readings: http://www.forgetit-project.eu Any questions to: allasia@eurix.it - walter.allasia@unito.it