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PROTAGE | Digital Preservation

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PROTAGE | Digital Preservation

  1. 1. Agents and Social Networks Environments for Digital Preservation Prof. Peplluis de la Rosa Albert Trias EASY INNOVA @ UdG Nov 2007 – Oct 2010 10/05/2013 1
  2. 2. PROTAGE Consortium PReservation Organizations using Tools in AGent Environments National Archives of Sweden Luleå University of Technology (Sweden) National Archives of Estonia Fraunhofer Gesellschaft zur Förderung der 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 3 Tones of personal data! DATA DELUGE (Fran Berman)
  4. 4. Opportunities Digital Preservation is a social duty; not only institutions but individuals 10/05/2013 5
  5. 5. Digital data paradigm shift 180 Exabytes 1600 Exabytes 9X Growth 95% Unstructured 70% Created by Individuals Enterprises responsible for 85% of this new data (Security, privacy, reliability, compliance) The Digital Data Paradigm Shift 2011 New Digital Data (25% Created, 75% Replicated) 2007 New Digital Data (Created, Captured, Replicated)
  6. 6. 10/05/2013 7 67% of DP expert users % think that the Digital Preservation solutions they have today are not good enough, or are insufficient (43%) or scarce (23%). So they look for new DP solutions
  7. 7. DP is social Fragmented DP Knowledge • 67% of expert users look for solutions provided by other institutions. • 90% of them consult trusted colleagues. • 83% consult final users Frequent Knowledge Exchanges • 83% of expert users share their knowledge • 77% search through the web for solutions • 60% visit DP web sites • 20% contribute to the web sites 10/05/2013 8 community of DP experts are in favour of DP knowledge exchanges with colleagues and other institutions, and they are equally happy to do it with individual users
  8. 8. DP Price • Distribution chanel of DP: there is slight preference for being bundled with storage services (77%) rather than being bundled with antivirus tools (63%). The key issue is that PROTAGE will attract DP knowledge exchange among expert users through Internet though it will also distribute the DP knowledge and solutions to individual users via storage software and antivirus tool bundles. The expert users (64%) claim they would accept to pay 10% of the price of the storage service, being the estimated 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 search Peer DP services like • ”keeping your copies as you keep mine” • Crossed services before preservation (i.e., enhancing content) 10/05/2013 10 Users show a slight preference for DP being bundled with storage services (77%) rather than being bundled with antivirus tools (63%). Expert users (64%) claim they would accept to pay 10% of the price of the storage service, being the estimated price of 15% of the storage service and 13% of the antivirus service.
  10. 10. PROTAGE–intelligent agents • Social search is implemented • Experts and final users share DP solutions • Lists of trust guide the social search It might seem the Facebook of DP • Agents automate the social search for DP solutions in terms of actions plans and migration support • Agents proactively schedule the preservation tasks Agents are a type of peer services 10/05/2013 11
  11. 11. First time a DP effort that targets not only memory institutions The prototype = IT Innovation Agent technology can be applied to DP Agent technology can simplify DP for many users and groups … PROTAGE = Intelligent Agents WHY AGENTS? 1210/05/2013
  12. 12. PROTAGE = Intelligent Agents WHY AGENTS? 13 •Agents: • Are reactive: react after receiving a question. • Are social: capability to communicate to others. • Are proactive: take initiative. • Are autonomous: ability to work independently. • Agents enable the construction of information systems from multiple heterogeneous sources [Dignum 2005] 10/05/2013
  13. 13. How Agents do Social Search? 14 When 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 Example 15 ActionPlan Eloy Albert Alex Access- Point Image conversion (3versions) X X X Tech medatada extraction X X X Local AV Check X X Local AV Clean X X Remote AV Check X X Remote AV Clean X X Generic Image Conversion (certified) X Calc MD5 X 1. Alex searches the keyword “AV”. in the “Application local DB” No result is found. Alex’s SA Eloy’s SA Albert’s SA AV Local DB 10/05/2013
  15. 15. Search Example 15 ActionPlan Eloy Albert Alex Access- Point Image conversion (3versions) X X X Tech medatada extraction X X X Local AV Check X X Local AV Clean X X Remote AV Check X X Remote AV Clean X X Generic Image Conversion (certified) X Calc MD5 X 1. Alex searches the keyword “AV” in the “Application local DB” No result is found. 2. Alex’s Agent searches for a certified actionplan into the Access Point (institutional point of view, with certified plans). No result is found again. Alex’s SA Eloy’s SA Albert’s SA AV Access Point DB 10/05/2013
  16. 16. Search Example 15 ActionPlan Eloy Albert Alex Access- Point Image conversion (3versions) X X X Tech medatada extraction X X X Local AV Check X X Local AV Clean X X Remote AV Check X X Remote AV Clean X X Generic Image Conversion (certified) X Calc MD5 X 1. Alex searches the keyword “AV” in the “Application local DB” No result is found. 2. Alex’s Agent searches for a certified actionplan into the Access Point (institutional point of view, with certified plans). No result is found again. 3. The Agent asks its friend Eloy (distance = 1). Alex’s SA Eloy’s SA Albert’s SA AV 10/05/2013
  17. 17. Search Example 15 ActionPlan Eloy Albert Alex Access- Point Image conversion (3versions) X X X Tech medatada extraction X X X Local AV Check X X Local AV Clean X X Remote AV Check X X Remote AV Clean X X Generic Image Conversion (certified) X Calc MD5 X 1. Alex searches the keyword “AV” in the “Application local DB” No result is found. 2. Alex’s Agent searches for a certified actionplan into 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 plans are good for Alex (trust-guided decision). Two plans (collection and author) do not match; their fulfilment 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 collection match higher than .50 of the QoS). Eloy’s Agent sends these 2 actionplans to Alex. Local DB Alex’s SA Eloy’s SA Albert’s SA AV 2 results found 10/05/2013
  18. 18. Search Example 15 ActionPlan Eloy Albert Alex Access- Point Image conversion (3versions) X X X Tech medatada extraction X X X Local AV Check X X Local AV Clean X X Remote AV Check X X Remote AV Clean X X Generic Image Conversion (certified) X Calc MD5 X 1. Alex searches the keyword “AV” in the “Application local DB” No result is found. 2. Alex’s Agent searches for a certified actionplan into 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 plans are good for Alex (trust-guided decision). Two plans (collection and author) do not match; their fulfilment 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 collection match higher than .50 of the QoS). Eloy’s Agent sends these 2 actionplans to Alex. 6. Alex’s Agent receives the actionplans from Eloy’s agent and ranks them for Alex. 7. The plans are added to Alex’s actionplan collection and are tagged “Eloy” as provider and author. Alex Eloy Albert Local DBAdd and Rate new plans 10/05/2013
  19. 19. Search Example 15 ActionPlan Eloy Albert Alex Access- Point Image conversion (3versions) X X X Tech medatada extraction X X X Local AV Check X X Local AV Clean X X Remote AV Check X X Remote AV Clean X X Generic Image Conversion (certified) X Calc MD5 X 4. If Eloy’s Agent did not found an Action Plan, then: 1. Eloy’s Agent will show to Eloy the question 2. Eloy’s Agent will forward the question to Albert’s Agent. Alex Eloy Albert 10/05/2013 AV AV
  20. 20. PROTAGE Client Application Collection and User Profiles Searcher Agent Searcher Agent Actions and Action Plans Memory institution Actions and Action Plans Searcher Agent PROTAGE Client Application Actions and Action Plans WS WS Execution agent WS WS WS WS WS WSWS WS WS WS WS WS WS Gateway Agent Institutional Agent Memory institution Institutional Agent Migration Agent Planets Core Registry, Blogs, Forums External knowledge bases Harvesting Agent 1619.01.2011
  21. 21. What we learned: the DP relevance • Novel approach, that way never seen before (both the approach and the prototypic solution) • Adequate services (tools) offered for organizational and individual users alike • Certain degree of “intelligence” shown by the solution • Step forward in bringing software agent technology into 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 the solution (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 preservation intelligence” embedded into the agents. 3. The system should be able to incorporate and analyze user’s collections. This adds to the aspect of personalization. 4. The system should make sure that it is able to efficiently point at particular problems in the domain, to problematic overall areas, and to specific potential risks. 5. The users need more help in formulating questions for searching for existing action plans. 6. A solution dedicated to “ordinary” private users with little or no experience in digital preservation requires the provision of a user- adapted graphical user interface with more predefined customizations but it pays off. 7. The solution must provide specific features for memory institutions allowing them to integrate the agent based technology into their daily procedures. 1819.01.2011
  23. 23. Main Achievements of the Project (2) AUTOMATION - Execution of DP tools (locally and as web services). - Technology watch function (through monitoring agents). (4) TRUST MODEL - Trustable access to trusted information on DP. (5) AWARENESS - An understanding of others’ activities that bring context to ones own activities (1) AGENTS - Agent ecosystem as a design concept for DP tools. - Provide context-sensitive access to DP information from trusted knowledge bases. (3) KNOWLEDGE MANAGEMENT - Reduces the knowledge gap between MIs and other users - Provides new means for MIs to reach their ”clients” - Reach more user groups. - Practical means for sharing DP information through social networks and crowd-sourcing MI = memory institutions DP = digital preservation PROTAGE 1919.01.2011
  24. 24. Future Technology Challenges User adapted GUIs Specific features for memory institutions Expand the intelligence potential – Pro-active solution Analyze users´ collections Point at problem areas/risks Help formulating questions Identify other domains 2019.01.2011
  25. 25. Thank you Peplluis de la Rosa and Albert Trias peplluis@eia.udg.edu albert.trias@udg.edu 10/05/2013 21

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