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Primer Taller en Grid Computing Universidad del Valle, Cali, Colombia January 2007 Semantic-OGSA www.ontogrid.eu Oscar Corcho University of Manchester.

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Presentation on theme: "Primer Taller en Grid Computing Universidad del Valle, Cali, Colombia January 2007 Semantic-OGSA www.ontogrid.eu Oscar Corcho University of Manchester."— Presentation transcript:

1 Primer Taller en Grid Computing Universidad del Valle, Cali, Colombia January 2007 Semantic-OGSA Oscar Corcho University of Manchester

2 2Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Outline  OntoGrid and Semantic-OGSA (S-OGSA)  The S-OGSA model  S-OGSA capabilities and mechanisms  Lifetime specification  S-OGSA scenarios of use  Semantic Provisioning Services  Conclusions and Future Work

3 3Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 EU-STREP Project OntoGrid  Middleware for the Semantic Grid  Metadata Storage & Querying  Ontology Access  Annotation Data and provenance Services  Business Process Monitoring  Negotiation and Coordination  Applications  Insurance Settlement  Satellite Image Quality Analysis  SEMANTIC OGSA  Capabilites & Behaviors for Semantic Grids  Principled way of realization And other applications being analysed

4 4Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Combe Chem Semantic Grid trajectory Time Efforts Implicit Semantics 1 st generation SRB Implicit Semantics OGSA generation GGF Semantic Grid Research Group Many workshops Systematic Investigation Phase Specific experiments Part of the Architecture Dagstuhl Seminar Grid Resource Ontology Semantic Grid workshops Pioneering Phase Ad-hoc experiments, early pioneers Demonstration Phase

5 5Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 From the pioneering phase to the systematic investigation phase  In the pioneering phase...  Ontologies and their associated technologies are not completely integrated in the Grid applications They are used as in Semantic Web applications  But there are distinctive features of Grid applications Distribution of resources Scale Resource management and state... (non exhaustive and non compulsory list)  In the systematic investigation phase  We have to take these features into account  And incorporate semantics as another Grid resource  Our proposal is: S-OGSA

6 6Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Design Principles Conceptual: reference architecture that can be applied to any grounding (WSRF, WS-Man, WS-I+, etc.) Parsimony: Architecture as lightweight as possible: minimise the impact on tooling, not dictate content Extensibility: Extensible and customisable as opposed to complete and generic architecture Diversity : Mixed ecosystem of Grid and Semantic Grid services. Semantics Ignorant, Semantics aware but incapable, Semantics aware and capable Uniformity: Everything is OGSA compliant. Our services are Grid services, knowledge and Metadata are Grid Resources. Multiform-Multiplicity: Any resource can have multiple descriptions and any description can be in different formalisms Enlightenment: Straightforward migration path

7 7Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA  Semantic-OGSA (S-OGSA) is...  Our proposed Semantic Grid reference architecture  A low-impact extension of OGSA Mixed ecosystem of Grid and Semantic Grid services  Services ignorant of semantics  Services aware of semantics but unable to process them  Services aware of semantics and able to process (part of) them Everything is OGSA compliant  Defined by Information model  New entities Capabilites  New functionalities Mechanisms  How it is delivered Model Capabilities Mechanisms provide/ consume expose use

8 8Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Model. Semantic Bindings Model MechanismsCapabilities

9 9Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 METADATA as Semantic Annotations S-OGSA Model Example Model MechanismsCapabilities

10 10Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Optimization Execution Management Resource management Data Security Information Management Infrastructure Services Application 1Application N OGSA Semantic-OGSA Semantic Provisioning Services From OGSA to the S-OGSA Ontology Reasoning Knowledge Metadata Annotation Semantic binding Semantic Provisioning Services Model MechanismsCapabilities

11 11Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Semantic Provisioning Service Knowledge Resource Grid Entity Semantic Binding Grid Service Is-a 0..m 1..m Semantic aware Grid Service consumeproduce 0..m 1..m uses WebMDS SAML file DFDL file JSDL file Is-a Knowledge Entity Is-a Ontology Service Is-a Reasoning Service Semantic Binding Provisioning Service Annotation Service Metadata Service Grid Resource OGSA-DAI CAS Is-a Knowledge Service Is-a Ontology Rule set KnowledgeSemantic GridGrid S-OGSA Model and Capabilities. The complete picture Model MechanismsCapabilities

12 12Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 OntoKit: An implementation of S-OGSA

13 13Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 OntoKit: An implementation of S-OGSA Ontology Role-based AuthZ Semantically Aware

14 14Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Patterns. Semantic-ignorant service Lifetime Metadata Service Ontology Service Resource Metadata Seeking Client Properties Others…. Access/Query Metadata Refers to Resource props Model MechanismsCapabilities

15 15Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Patterns. Semantic Aware but Incapable Service Lifetime Metadata Service Ontology Service Resource Metadata Seeking Client Properties Others… Access/Query Semantic Bindings Refers to Get Semantic Binding Pointers 2 1 Resource properties Model MechanismsCapabilities

16 16Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Patterns. Semantic Aware and Capable Service Lifetime Metadata Service Resource Metadata Seeking Client Properties Others… Access/Query Semantic Bindings 1 Semantics 1.1 Farm out request Semantic aware interface Ontology Service Model MechanismsCapabilities

17 17Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Grounding. Grid Ontology and S-OGSA Ontology  Grid Ontology  Common set of ontologies to describe Grid entities (resources and services)  Based on work from UniGrids  Effort to be continued by OntoGrid  Available in OntoGrid’s CVS

18 18Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Metadata Access/Management Protocols SB Factory Client Semantic Binding Metadata Query SB create Query w/o Inference, UpdateContent Query( over unified view) WS-RP: Get/Set/Query Properties WS-Addressing: epr RDF create query Inspect- props... query Semantic Binding Service Suite WS-RL: Destroy, SetTerminationTime WS-RL ++: archive WS-Notif: Subscribe / Notify

19 19Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Semantic Binding Service. Lifetime Specification  What happens if... ...any or all of the Grid entities it refers to disappears? Instrument and planning files for satellites do not disappear Insurance contracts, cars, repair companies, etc., may disappear ...the Knowledge entities disappear or evolve? Ontologies may change ... a SB is no longer available (its content is not useful any more)? Damage claims: add witness reports, improve info about location, create new hypothesis...  When do/should SBs become invalid? How often should this be checked?  What is the status of the content of a SB (e.g., content checked, stable, unchecked, etc.)?  Is a SB always active or can it be archived after a period of time?  Satellite data that is not used after some time

20 20Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Semantic Binding Service. WS-SBResourceLifetime  Lifetime specification based on WS-ResourceLifetime  Extension with  Resource properties (state)  Updates  Archive  Notifications  SB Housekeeping service

21 21Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-SBResourceLifetime vs WS-ResourceLifetime WS-SBResourceLifetime - archive - setUpdateTime WS-ResourceLifetime - setTerminationTime - destroy Basic Operations - createSemanticBinding (Factory) - addGridEntityReference/removeGridEntityReference - addKnowledgeEntityReference/removeKnowledgeEntityReference - getContent - updateSBContent - query - queryWithInference

22 22Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Update Notifications  From entities to Semantic Binding SemanticBindingService EPR key key Lifetime Service Resource Properties Others…. Resource props Polling of resource property [lastModificationTime] Grid Entity Knowledge Entity Lifetime Service Resource Properties Others…. Resource props Polling of resource property [lastModificationTime]

23 23Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Update Notifications  Semantic Binding Update  Description: Updates in the content or in the state of a Semantic Binding  Message content: updateTime updateType [stateChange,contentChange] newState [any of the ones defined in the state machine] updateReason SemanticBindingService MetadataService EPR key key SBHouseKeepingService Check that SB content is still valid

24 24Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Outline  Background  The Grid and its characteristics  Open Grid Services Architecture-OGSA  Grid Standardization Activities  Semantic Grid  OntoGrid and Semantic-OGSA (S-OGSA)  The S-OGSA model  S-OGSA capabilities and mechanisms  Lifetime specification  S-OGSA scenarios of use  Semantic Provisioning Services  Conclusions and Future Work

25 25Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues Space Segment Ground Segment DMOP files Product files SATELLITE FILES:

26 26Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues  Comparison between planning and product generation:... Instr#n (RA_2) planning DMOP_File#n(StartTime)DMOP_File#n(StopTime) DMOP_File#(n+1) StartTime DMOP#(n+1)_ File (StopTime) DMOP_er (ORBIT_NUMBER, ELAPSED_TIME) Instr#1 planning DURATION PRODUCT_FILE Start_time (SENSING_START) PRODUCT_FILE Stop_time (SENSING_STOP)... Instr#n(RA_2) Product Generation RA2_CAL_1P Stop_time (SENSING_STOP) RA2_CAL_1P Start_time (SENSING_START) PRODUCT_data_gap...

27 27Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues

28 28Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Deimos Integrated Prototype WebDAV WS-DAIOnt-RDF(S) SatelliteDomain Ontology Satellite File WebDAV client e.g. MS Windows Explorer HTTP PUT Metadata Service QUARC-SG client JSP 2 UTC2Seconds Soaplab Convert time to canonical representation Annotate file Obtain ontology Type metadata Store Query Convert time to canonical representation Input criteria Copy satellite file Metadata generation process Metadata querying process

29 29Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues Satellite files:  DMOP (PLANNING) FILES FILE ; DMOP (generated by FOS Mission Planning System) RECORD fhr FILENAME="DMOP_SOF__VFOS _103709_ _ _ _014048_ _ N1" DESTINATION="PDCC" PHASE_START=2 CYCLE_START=44 REL_START_ORBIT=404 ABS_START_ORBIT=20498 ENDRECORD fhr RECORD dmop_er RECORD dmop_er_gen_part RECORD gen_event_params EVENT_TYPE=RA2_MEA EVENT_ID="RA2_MEA_ " NB_EVENT_PR1=1 NB_EVENT_PR3=0 ORBIT_NUMBER=20521 ELAPSED_TIME= DURATION= ENDRECORD gen_event_params ENDRECORD dmop_er ENDLIST all_dmop_er ENDFILE RECORD ID RECORD parameters RECORD parameters corresponding to other RECORD structure.

30 30Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues Satellite Ontology (General view)

31 31Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues Satellite Ontology (Hierarchies)

32 32Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007  Planning (DMOP) RECORD parameters Satellite Use Case: Technical issues

33 33Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 "DMOP_SOF__VFOS _103709_ _ _ _014048_ _ N1" "PDCC" GOM_PAU "GOM_PAU_ " Satellite Use Case: Technical issues Satellite files: XMLed DMOP (PLANNING) FILES Not yet transformed RECORD parameters. Parameters NOT needed to be transformed at this moment RECORD ID transformed to XML RECORD parameters transformed

34 34Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite files: Annotated DMOP (PLANNING) FILES MS "GOM_OCC_ " [...] Satellite Use Case: Technical issues

35 35Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues Satellite files  PRODUCT FILES PRODUCT="RA2_MW__1PNPDE _231554_ _00416_20510_0181.N1" PROC_STAGE=N REF_DOC="PO-RS-MDA-GS-2009_3/M " SENSING_START="31-JAN :15: " SENSING_STOP="01-FEB :58: " PHASE=2 CYCLE=+044 REL_ORBIT= ABS_ORBIT= STATE_VECTOR_TIME="31-JAN :28: " DELTA_UT1= X_POSITION= Y_POSITION= Z_POSITION= X_VELOCITY= Y_VELOCITY= Z_VELOCITY= PRODUCT_ERR=0 TOT_SIZE= SPH_SIZE= NUM_DSD= DSD_SIZE= NUM_DATA_SETS= Parameters NOT needed to be transformed at the moment Parameters to be transformed Parameters to be transformed at this moment

36 36Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues Satellite files: XMLed PRODUCT FILE " RA2_MW__1PNPDE _231554_ _00416_20510_0181.N1“ N " PO-RS-MDA-GS-2009_3/M " " PDHS-E " " 01-FEB :22: " " RA2/5.02 " " 31-JAN :15: " " 01-FEB :58: " --> Parameters NOT needed to be transformed at this moment. !!! FUTURE SCALABILITY IMPROVEMENT !!

37 37Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007  Namefile (Product): RA2_MW__1PNPDK _120535_ _00424_20518_0349.N1 " Corresponds to: Satellite Use Case: Technical issues Satellite files: PRODUCT filename

38 38Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite files: Annotated PRODUCT FILE [...] "RA2_MW__1PNPDK _120535_ _00424_20518_0334.N1" "RA2_MW__1PNPDK _160340_ _00441_20535_0344.N1" [...] Satellite Use Case: Technical issues

39 39Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 // Use to get a proxy class for MetadataService private java.lang.String MetadataService_address = "http:// :8080/AtlasService/services/MetadataService";http:// :8080/AtlasService/services/MetadataService public java.lang.String getMetadataServiceAddress() { return MetadataService_address; } […] public eu.ist.ontogrid.ontokit.MetadataService.MetadataService getMetadataService() throws javax.xml.rpc.ServiceException { java.net.URL endpoint; try { endpoint = new java.net.URL(MetadataService_address); } catch (java.net.MalformedURLException e) { throw new javax.xml.rpc.ServiceException(e); } return getMetadataService(endpoint); } […] public static void main(String[] args) { MetadataServiceProxy proxy = new MetadataServiceProxy(); String query1 ="SELECT X FROM {X}kb:instrument_mode_id{Y} WHERE Y=\"STB\" USING NAMESPACE kb=&http://protege.stanford.edu/kb#"; String query2 = "SELECT Z FROM {X}kb:plan_file_name{Y},{Y}kb:file_id{Z}, {Y}kb:start_time{T1}, {Y}kb:stop_time{T2} WHERE T1> AND T AND T2< USING NAMESPACE kb=&http://protege.stanford.edu/kb#"; try { System.out.println("submitting test query"); String result = proxy.query(query2); System.out.println(result); AtlasResultSet results = new AtlasResultSet(result); Satellite Use Case: Technical issues Satellite files (Metadata Queries):

40 40Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Satellite Use Case: Technical issues  Timeline Planning-Product Generation:

41 41Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Insurance Grid  Business values: Value (cost reduction, billing) Time to market / speed of implementation Ahead of competitors Fit within (human and technical) organization Innovation drive  Solve existing problems: Making processes more efficient with a new approach (more) Reliable / Accepted Proven / Cheaper -> CarRepairGid  Solve problems that could not be solved before: Lack of trust/ Unfamiliar Politics Technical / organizational limitations -> CarFraudGrid

42 42Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Business Case 1: Car Repair Business Case  Context:  Repair damaged cars  Negotiation between insurance and repair company Speed, Price, Quality Method of repair, Selection of material,Paint, Coalition  Now:  negotiation by hand  long term (yearly)  Challenge:  Automated negotiation  short term (every claim)  Include SLA

43 43Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007  Approx damage per year in the Netherlands  Approx. 4 hours to handle damage report  Current technology insecure  Unable to handle complex control and data structure  Inflexible  FTE works for 1750 hours effectively a year  Costs of a FTE are 75k EURO  / 1750 = 230 FTEs  Save on manual labor: 230* 75 K = 173M  Expect repair prices to drop by 5-15% “best deal for every damage for all parties involved” This makes the CarRepairGrid a Business Case

44 44Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Scenario. Insurance settlement  Data and resources scenarios  Register Repair Co. contract at CarRepairGrid.  Select Repair Companies for negotiation  Metadata scenarios  Calculate offer by a Repair Company (damage report)  Judge Invoice sent by Repair Company  Process management scenarios  Multi issue negotiation between Repair Companies (repair)  Multi issue negotiation between >3 insurance companies (Recovery)  Services scenarios  Provide Policy Information  Check coverage  Security scenarios  Check client registration at insurance companies  Check Car Theft - automatic check on car identity i.e. frame numbers and parts

45 45Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 S-OGSA Scenario. Insurance settlement WS-DAIOnt Negotitation Service (Manager) Job Negotiation client 1 2 Do Negotiation Atlas RD F InsurranceCo DB Motor Vahicles Car Parts Repair CO. 1 (Nego. Srvc. Contractor) Repair CO. 2 (Nego. Srvc. Contractor) Repair CO. 3 (Nego. Srvc. Contractor) Job + Contractor List Job Cfp propose Offer Refuse propose Offer accept 5 Reject 5 WS-DAIOnt Car Repair DB RD F Car Repair DB 3 calculatePrice 3 3 Retrieve public Job desc. Legacy databases

46 46Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007  Situation: A lot of tricks to get money from insurance companies  Now: Ad hoc manual techniques Only pattern search on local or national scale Most tricks found on accident  Challenge: Automated fraud detection Business Case 2:CarFraudGrid

47 47Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Known trick: Berliner Model  Trick: Buy damaged expensive car Change some features Have stolen cars have accidents with it Claim money from insurance company of stolen car  Search for: Similar cars combined with similar situations combined with similar participants National / International scale

48 48Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Car Fraud Business Case Motivation

49 49Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Car Fraud Business Case Motivation

50 50Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Conceptual Architecture

51 51Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Suggested Approach: Use case

52 52Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Suggested Approach: Claim Assessment CommonKads Inference Model

53 53Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Suggested Approach: Fraud Diagnosis CommonKads Inference Model

54 54Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Domain model  Every insurance company uses its own database/domain model. Every claim database contains in some form important data about:  * cars  * situation  To find evidence we will look in claim history based on the current claim.  We look at car for: Car  * brand, e.g. Peugeot  * model, e.g. 307  * type, e.g. SW  * mileage  * license plate  * owner  * color  * chassisnumber  * constructionyear  * countryofregistration Situation  * place of damage (angle of impact)  * description of accident  * time of accident  * accident location  * price of damage  * damaged objects  * witnesses ?

55 55Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 This makes CarFraudGrid 2 a Business Case  7-10% of all claims involve a form of fraud  “Autobumsen”  400 ME in Germany (2004)  European Harmonization Law “Have independent trusted 3 rd party search for criminal patterns within insurance databases…”

56 56Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Ontology-based Role-based Authorisation  Insurance Security scenario cast as role based Grid Access Control Scenario.  Role based Access Control Policy is:  Good Reputation Drivers are allowed to ask for an insurance policy. Bad Reputation ones are not.  VO ontology based on  KaOS ontologies (Actors, Groups and Actions)  Role definitons Extend ontology with domain-specific classes and properties Define roles wrt these extensions  E.g., a blacklistedDriver is a driver that has had at least 3 accident claims in the past  E.g., a goodReputationDriver is a driver that has been insured at least by one trusted company and that has had at most 2 accident claims  The Access Control Function uses a DL classifier to obtain roles of a Subject.

57 57Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-DAIOnt XACML_AuthZService (PDP) CarFraudService (PEP) XACML AuthZ Request getInsurancePolicy VO Ontology Class Hierarchy -RDFS RD F John Doe has had 2 distinct accidents Role Op Mapping Pellet Reasoner Obtain Semantic Bindings of John Doe Obtain all classes that are subclass of ROLE Classify John Doe wrt VO ont Lookup whether the ROLE that is inferred permits or not XACML AuthZ Response Semantic Binding Service PIP Proxy PDP Proxy VO Ontology OWL S-OGSA Scenario. Authorisation 8 Result or Exception /C=GB/O=PERMIS/CN=User0

58 58Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-DAIOnt XACML_AuthZService (PDP) CarFraudService (PEP) XACML AuthZ Request getInsurancePolicy VO Ontology Class Hierarchy -RDFS RD F John Doe has had 2 distinct accidents Role Op Mapping Pellet Reasoner Obtain Semantic Bindings of John Doe Obtain all classes that are subclass of ROLE Classify John Doe wrt VO ont Lookup whether the ROLE that is inferred permits or not XACML AuthZ Response Semantic Binding Service PIP Proxy PDP Proxy VO Ontology OWL S-OGSA Scenario. Authorisation 8 Result or Exception

59 59Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-DAIOnt XACML_AuthZService (PDP) CarFraudService (PEP) XACML AuthZ Request getInsurancePolicy VO Ontology Class Hierarchy -RDFS RD F John Doe has had 2 distinct accidents Role Op Mapping Pellet Reasoner Obtain Semantic Bindings of John Doe Obtain all classes that are subclass of ROLE Classify John Doe wrt VO ont Lookup whether the ROLE that is inferred permits or not XACML AuthZ Response Semantic Binding Service PIP Proxy PDP Proxy VO Ontology OWL S-OGSA Scenario. Authorisation 8 Result or Exception

60 60Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-DAIOnt XACML_AuthZService (PDP) CarFraudService (PEP) XACML AuthZ Request getInsurancePolicy VO Ontology Class Hierarchy -RDFS RD F John Doe has had 2 distinct accidents Role Op Mapping Pellet Reasoner Obtain Semantic Bindings of John Doe Obtain all classes that are subclass of ROLE Classify John Doe wrt VO ont Lookup whether the ROLE that is inferred permits or not XACML AuthZ Response Semantic Binding Service PIP Proxy PDP Proxy VO Ontology OWL S-OGSA Scenario. Authorisation 8 Result or Exception

61 61Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-DAIOnt XACML_AuthZService (PDP) CarFraudService (PEP) XACML AuthZ Request getInsurancePolicy VO Ontology Class Hierarchy -RDFS RD F John Doe has had 2 distinct accidents Role Op Mapping Pellet Reasoner Obtain Semantic Bindings of John Doe Obtain all classes that are subclass of ROLE Classify John Doe wrt VO ont Lookup whether the ROLE that is inferred permits or not XACML AuthZ Response Semantic Binding Service PIP Proxy PDP Proxy VO Ontology OWL S-OGSA Scenario. Authorisation 8 Result or Exception

62 62Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 WS-DAIOnt XACML_AuthZService (PDP) CarFraudService (PEP) XACML AuthZ Request getInsurancePolicy VO Ontology Class Hierarchy -RDFS RD F John Doe has had 2 distinct accidents Role Op Mapping Pellet Reasoner Obtain Semantic Bindings of John Doe Obtain all classes that are subclass of ROLE Classify John Doe wrt VO ont Lookup whether the ROLE that is inferred permits or not XACML AuthZ Response Semantic Binding Service PIP Proxy PDP Proxy VO Ontology OWL S-OGSA Scenario. Authorisation 8 Result or Exception Ignorant of semantics Semantic aware and capable of processing semantics Semantic provisioning services Semantic aware but incapable of processing semantics

63 63Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Data Integration  Information integration from gLite and GT4 information services  BDII  RGMA  MDS  Trade-off between...  Continuous update or on- demand access, fresh information  Consolidated data but possibly non-fresh information

64 64Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Outline  Background  The Grid and its characteristics  Open Grid Services Architecture-OGSA  Grid Standardization Activities  Semantic Grid  OntoGrid and Semantic-OGSA (S-OGSA)  The S-OGSA model  S-OGSA capabilities and mechanisms  Lifetime specification  S-OGSA scenarios of use  Semantic Provisioning Services  Conclusions and Future Work

65 65Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Conclusions  A principled Semantic Grid reference architecture  Low-impact extension of OGSA  Mixed ecosystem of Grid and Semantic Grid services  Ontology and metadata technology... ... can be used in Grid applications ... has to be adapted for its use in Grid environments Grid-compliant (provide Grid protocols, interfaces, etc.) Grid-aware (use of Grid technology)  First use cases being deployed  Still far from large-scale (production) deployment

66 66Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Our future work  Semantic Binding Service  Lifetime management  Fine-grained AuthZ  Prototypes demonstrating Knowledge-Aware Grid Services  OGSA-DAI semantic extensions  EGEE information service consolidating heterogeneous information sources  Meta-scheduler using semantic technologies  Enlightenment  Guidelines about how and when to apply semantic technologies in Grid systems

67 67Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 More information  Publications  An overview of S-OGSA: a Reference Semantic Grid Architecture. Corcho O, Alper P, Kotsiopoulos I, Missier P, Bechhofer S, Goble C. Journal of Web Semantics 4(2): June 2006  Deliverable D1.2v2  Source code  For Downloading Distributions  Access to CVS Connection type: pserver user: ontogrid password: not needed Host: rpc262.cs.man.ac.uk Port: 2401 Repository path: /local/ontogrid/cvsroot module: prototype

68 68Primer Taller en Grid Computing. Universidad del Valle, Cali, Colombia. January 2007 Questions  Thank you for your attention!  Questions?  Acknowledgements  OntoGrid Consortium Pinar Alper, Ioannis Kotsiopoulos, Paolo Missier, Wei Xing, Ian Dunlop, Sean Bechhofer, Carole Goble

69 Primer Taller en Grid Computing Universidad del Valle, Cali, Colombia January 2007 Semantic-OGSA Oscar Corcho University of Manchester


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