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RDF triple store

Web interface. Query system. RDF triple store. Curator. Research grants databases. Departmental Web sites. Harvester. Harvester. Ontology. request interaction model. OpenKnowledge plugin supplier. request plugin. interpret interaction model. routing. share interaction model.

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RDF triple store

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  1. Web interface Query system RDF triple store Curator Research grants databases Departmental Web sites Harvester Harvester Ontology

  2. request interaction model OpenKnowledge plugin supplier request plugin interpret interaction model

  3. routing share interaction model

  4. ask(p(Y)) 1 Peer p2 tell(p(a)) 2 Peer p1 know(p(a)) query_from(p(Y), p2) query_from(q(Z), p3) Peer p3 4 tell(q(b)) 3 know(q(b)) ask(q(Z))

  5. a(requester([q(ask(p(X)),p2),q(ask(q(Y)),p3)]), p1) a(informer, p2) ask(p(X)) => a(informer, p2) ask(p(X)) <= a(requester(([q(ask(p(X)),p2),q(ask(q(Y)),p3)]), p1) tell(p(a)) <= a(informer, p2) tell(p(a)) <= a(requester(([q(ask(p(X)),p2),q(ask(q(Y)),p3)]), p1) a(requester([q(ask(q(Y)),p3)]), A) a(informer, p3) ask(q(Y)) => a(informer, p3) ask(q(Y)) <= a(requester(([q(ask(q(Y)),p3)]), p1) tell(q(b)) <= a(informer, p3) tell(q(b)) <= a(requester(([q(ask(q(Y)),p3)]), p1)

  6. a(r(A), X) ::= ( R(A, Ar) a(r(Ar), X) ) or ( P(A) ) a(r(A), X) ::= ( M => a(r2, p2) <-- A = [M|Ar] then a(r(Ar), X) ) or ( null <-- A = [] )

  7. a(r(A), X) ::= ( M => a(r2, p2) <-- A = [M|Ar] then R <= a(r2, p2) then a(r(Ar), X) ) or ( null <-- A = [] )

  8. a(r(A, B), X) ::= ( M => a(r2, p2) <-- A = [M|Ar] then B = [R|Br]<-- R <= a(r2, p2) then a(r(Ar, Br), X) ) or ( null <-- A = [] and B = [] )

  9. Scientists Service invocation Curated database services

  10. sharing data collating Proxies (no need for database curators to know)

  11. LCC Interaction Specification: Data Collator a(data_collator(Seq,Best), C) :: filter_results(Seq,Results,Best)  a(poller(Seq,Peers,Results), C)  sources(Peers) a(poller(Seq,Peers,Results), C) :: ( a(data_seeker(Seq,D,Matches), C)  Peers = [D|RestPeers] and Results = [r(D,Matches)|RestResults] then a(poller(Seq,RestPeers,RestResults), C) ) or null  Peers = [] and Results = []. a(data_seeker(Seq,D,Matches), S) :: query(Seq) => a(data_source, D) then filter_matches(Seq,Results,Matches)  matched(Results) <= a(data_source, D). You can be a data collator for a sequence, seeking the best matches of you can filter the results from polling your peers for their best matches You can poll a set of your peers for their best results if you become a data seeker for the first of these peers and the matches from that peer are merged with the matches you get from polling the rest of the set of peers; or if the set of peers is empty you have no matches You can be a data seeker asking a peer for a set of matches if you send a message to that peer asking it to be a data source then filter the matches it sends back to you in its response data_source data_source data_source

  12. LCC Interaction Specification: Data Sharing sharing a(data_source, D) :: query(Seq) <= a(data_seeker(Seq,D,Matches), S) then ( matched(Results) => a(data_seeker(Seq,D,Matches), S)  matching_sequences(Seq,Results) or ( a(data_collator(Seq,Results), D)  not(matching_sequences(Seq,_)) then matched(Results) => a(data_seeker(Seq,D,Matches), S) ) ). You can be a data source if you get a query from a data seeker and then you send out the matching sequences to the data seeker if these matching sequences can be obtained locally; or you become a data collator, seeking results from others if there are no local matching sequences and then you send out these matching sequences to the data seeker.

  13. Example: Yeast Protein Data sharing consistency checking SWISS SAM ModBase

  14. Coordinating peer Interaction model Simulated agents Environment simulator

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