PROTAGE | Digital Preservation

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Agents and Social Networks Environments for Digital Preservation

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PROTAGE | Digital Preservation

  1. 1. Agents and Social NetworksEnvironments for DigitalPreservationProf. Peplluis de la RosaAlbert TriasEASY INNOVA @ UdGNov 2007 – Oct 201010/05/2013 1
  2. 2. PROTAGE ConsortiumPReservation Organizations using Tools in AGentEnvironmentsNational Archives of SwedenLuleå University of Technology (Sweden)National Archives of EstoniaFraunhofer Gesellschaft zur Förderungder angewandten Forschung e.V. (Germany)University of Bradford (U. K.)EASY Innova @ UdG (Spain)Giunti Labs S.r.l. (Italy)
  3. 3. Digital Preservation?10/05/2013 3Tones of personal data!DATA DELUGE(Fran Berman)
  4. 4. OpportunitiesDigital Preservation is a social duty;not only institutions but individuals10/05/2013 5
  5. 5. Digital data paradigm shift180 Exabytes1600 Exabytes9X Growth95%Unstructured70% Created by Individuals Enterprises responsible for 85% of this new data(Security, privacy, reliability, compliance)The Digital Data Paradigm Shift2011 New Digital Data(25% Created, 75% Replicated)2007 New Digital Data(Created, Captured, Replicated)
  6. 6. 10/05/2013 767% of DP expert users % think that theDigital Preservation solutions they havetoday are not good enough, or areinsufficient (43%) or scarce (23%). Sothey look for new DP solutions
  7. 7. DP is socialFragmented DPKnowledge• 67% of expert users lookfor solutions provided byother institutions.• 90% of them consulttrusted colleagues.• 83% consult final usersFrequent KnowledgeExchanges• 83% of expert usersshare their knowledge• 77% search through theweb for solutions• 60% visit DP web sites• 20% contribute to theweb sites10/05/2013 8community of DP experts are in favour of DP knowledge exchanges withcolleagues and other institutions, and they are equally happy to do it withindividual users
  8. 8. DP Price• Distribution chanel of DP: there is slight preference forbeing bundled with storage services (77%) rather thanbeing bundled with antivirus tools (63%). The key issueis that PROTAGE will attract DP knowledge exchangeamong expert users through Internet though it will alsodistribute the DP knowledge and solutions to individualusers via storage software and antivirus tool bundles.The expert users (64%) claim they would accept to pay10% of the price of the storage service, being theestimated price of 15% of the storage service and 13%of the antivirus service.10/05/2013 9
  9. 9. DP Opportunities• DP Consultancy• Indexing: deep web• Social searchPeer DP services like• ”keeping your copiesas you keep mine”• Crossed servicesbefore preservation(i.e., enhancingcontent)10/05/2013 10Users show a slight preference for DP being bundled with storageservices (77%) rather than being bundled with antivirus tools (63%).Expert users (64%) claim they would accept to pay 10% of the priceof the storage service, being the estimated price of 15% of thestorage service and 13% of the antivirus service.
  10. 10. PROTAGE–intelligent agents• Social search isimplemented• Experts and finalusers share DPsolutions• Lists of trust guide thesocial searchIt might seem theFacebook of DP• Agents automatethe social search forDP solutions in termsof actions plans andmigration support• Agents proactivelyschedule thepreservation tasksAgents are a type of peerservices10/05/2013 11
  11. 11. First time aDP effort thattargets notonly memoryinstitutionsThe prototype =IT InnovationAgent technology canbe applied to DPAgent technology cansimplify DP for manyusers and groups …PROTAGE = Intelligent AgentsWHY AGENTS?1210/05/2013
  12. 12. PROTAGE = Intelligent AgentsWHY AGENTS?13•Agents:• Are reactive: react after receiving a question.• Are social: capability to communicate toothers.• Are proactive: take initiative.• Are autonomous: ability to workindependently.• Agents enable the construction of informationsystems from multiple heterogeneous sources[Dignum 2005]10/05/2013
  13. 13. How Agents do Social Search?14When a User is searching for a DP Plan, the query is sent to her Searcher Agent.The Searcher Agent:• Search in the local Knowledge Base• Search in Institution Repositories Knowledge Base• Asks friends’ agents (send and forward message)• Filter Results:• Only DP Plans well rated by friends• Only DP which are owned by users of with some features.• Hybrid• Can show the question to its user.• The effort of a search depends on the trust it has to the sender.10/05/2013
  14. 14. Search Example15ActionPlan Eloy Albert Alex Access-PointImage conversion (3versions) X X XTech medatada extraction X X XLocal AV Check X XLocal AV Clean X XRemote AV Check X XRemote AV Clean X XGeneric Image Conversion (certified) XCalc MD5 X1. Alex searches the keyword “AV”.in the “Application local DB” No result is found.Alex’s SA Eloy’s SA Albert’s SAAVLocal DB10/05/2013
  15. 15. Search Example15ActionPlan Eloy Albert Alex Access-PointImage conversion (3versions) X X XTech medatada extraction X X XLocal AV Check X XLocal AV Clean X XRemote AV Check X XRemote AV Clean X XGeneric Image Conversion (certified) XCalc MD5 X1. Alex searches the keyword “AV” in the“Application local DB” No result is found.2. Alex’s Agent searches for a certified actionplaninto the Access Point (institutional point of view,with certified plans). No result is found again.Alex’s SA Eloy’s SA Albert’s SAAVAccess Point DB10/05/2013
  16. 16. Search Example15ActionPlan Eloy Albert Alex Access-PointImage conversion (3versions) X X XTech medatada extraction X X XLocal AV Check X XLocal AV Clean X XRemote AV Check X XRemote AV Clean X XGeneric Image Conversion (certified) XCalc MD5 X1. Alex searches the keyword “AV” in the“Application local DB” No result is found.2. Alex’s Agent searches for a certified actionplaninto the Access Point (institutional point ofview, with certified plans). No result is foundagain.3. The Agent asks its friend Eloy (distance = 1).Alex’s SA Eloy’s SA Albert’s SAAV10/05/2013
  17. 17. Search Example15ActionPlan Eloy Albert Alex Access-PointImage conversion (3versions) X X XTech medatada extraction X X XLocal AV Check X XLocal AV Clean X XRemote AV Check X XRemote AV Clean X XGeneric Image Conversion (certified) XCalc MD5 X1. Alex searches the keyword “AV” in the“Application local DB” No result is found.2. Alex’s Agent searches for a certified actionplaninto the Access Point (institutional point of view,with certified plans). No result is found again.3. The Agent asks its friend Eloy (distance = 1).4. Eloy’s agent checks whether 4 matching plansare good for Alex (trust-guided decision). Twoplans (collection and author) do not match; theirfulfilment F = .35 is lower than the QoS of .50(the Quality of Service threshold).5. On the other hand, two trusted plans match(fulfilment F=1.00 as author and collectionmatch higher than .50 of the QoS). Eloy’sAgent sends these 2 actionplans to Alex. Local DBAlex’s SA Eloy’s SA Albert’s SAAV2 resultsfound10/05/2013
  18. 18. Search Example15ActionPlan Eloy Albert Alex Access-PointImage conversion (3versions) X X XTech medatada extraction X X XLocal AV Check X XLocal AV Clean X XRemote AV Check X XRemote AV Clean X XGeneric Image Conversion (certified) XCalc MD5 X1. Alex searches the keyword “AV” in the“Application local DB” No result is found.2. Alex’s Agent searches for a certified actionplaninto the Access Point (institutional point ofview, with certified plans). No result is foundagain.3. The Agent asks its friend Eloy (distance = 1).4. Eloy’s agent checks whether 4 matching plansare good for Alex (trust-guided decision). Twoplans (collection and author) do not match; theirfulfilment F = .35 is lower than the QoS of .50(the Quality of Service threshold).5. On the other hand, two trusted plans match(fulfilment F=1.00 as author and collectionmatch higher than .50 of the QoS). Eloy’sAgent sends these 2 actionplans to Alex.6. Alex’s Agent receives the actionplans fromEloy’s agent and ranks them for Alex.7. The plans are added to Alex’s actionplancollection and are tagged “Eloy” as provider andauthor. Alex Eloy AlbertLocal DBAdd and Ratenew plans10/05/2013
  19. 19. Search Example15ActionPlan Eloy Albert Alex Access-PointImage conversion (3versions) X X XTech medatada extraction X X XLocal AV Check X XLocal AV Clean X XRemote AV Check X XRemote AV Clean X XGeneric Image Conversion (certified) XCalc MD5 X4. If Eloy’s Agent did not found an Action Plan, then:1. Eloy’s Agent will show to Eloy thequestion2. Eloy’s Agent will forward the question toAlbert’s Agent.Alex Eloy Albert10/05/2013AVAV
  20. 20. PROTAGE Client ApplicationCollection andUser ProfilesSearcherAgentSearcherAgentActions andAction PlansMemory institutionActions andAction PlansSearcherAgentPROTAGE Client ApplicationActions andAction PlansWSWSExecutionagentWSWSWSWSWSWSWSWSWSWSWSWSWSGatewayAgentInstitutionalAgentMemory institutionInstitutionalAgentMigrationAgentPlanetsCoreRegistry,Blogs,ForumsExternalknowledge basesHarvestingAgent1619.01.2011
  21. 21. What we learned: the DP relevance• Novel approach, that way never seen before (both theapproach and the prototypic solution)• Adequate services (tools) offered for organizationaland individual users alike• Certain degree of “intelligence” shown by the solution• Step forward in bringing software agent technologyinto DP domain (not yet reached the end of that road)• Personalization of user access to solution (profile,preferences, own resources)• No prerequisite for expertise in DP before using thesolution (depends on complexity of action plans)1719.01.2011
  22. 22. Future work (PROTAGE++)1. The pro-activity of the solution is essential even if a “one-size-fits-all” solution is not expected to exist.2. There is a need for expanding the potential of “digital preservationintelligence” embedded into the agents.3. The system should be able to incorporate and analyze user’scollections. This adds to the aspect of personalization.4. The system should make sure that it is able to efficiently point atparticular problems in the domain, to problematic overall areas,and to specific potential risks.5. The users need more help in formulating questions for searchingfor existing action plans.6. A solution dedicated to “ordinary” private users with little or noexperience in digital preservation requires the provision of a user-adapted graphical user interface with more predefinedcustomizations but it pays off.7. The solution must provide specific features for memory institutionsallowing them to integrate the agent based technology into theirdaily procedures.1819.01.2011
  23. 23. Main Achievements of the Project(2) AUTOMATION- Execution of DPtools (locally and asweb services).- Technology watchfunction (throughmonitoring agents).(4) TRUST MODEL- Trustable access totrusted information onDP.(5) AWARENESS- An understanding ofothers’ activities thatbring context to onesown activities(1) AGENTS- Agent ecosystem asa designconcept for DP tools.- Provide context-sensitiveaccess to DP informationfrom trusted knowledgebases.(3) KNOWLEDGEMANAGEMENT- Reduces the knowledge gapbetween MIs and other users- Provides new means for MIsto reach their ”clients”- Reach more user groups.- Practical means for sharingDP information through socialnetworks and crowd-sourcingMI = memoryinstitutionsDP = digitalpreservationPROTAGE1919.01.2011
  24. 24. Future Technology ChallengesUser adapted GUIs Specific features formemory institutionsExpand the intelligence potential –Pro-active solution Analyze users´collectionsPoint at problemareas/risksHelp formulatingquestionsIdentify otherdomains2019.01.2011
  25. 25. Thank youPeplluis de la Rosa and Albert Triaspeplluis@eia.udg.edualbert.trias@udg.edu10/05/2013 21

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