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(Semantic Grid) Services + Semantic (Grid Services)

(Semantic Grid) Services + Semantic (Grid Services). Professor Carole Goble The University of Manchester, UK e-Science North West Regional Centre my Grid, OntoGrid, Knowledge Web GGF Semantic Grid Research Group.

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(Semantic Grid) Services + Semantic (Grid Services)

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  1. (Semantic Grid) Services + Semantic (Grid Services) Professor Carole Goble The University of Manchester, UK e-Science North West Regional Centre myGrid, OntoGrid, Knowledge Web GGF Semantic Grid Research Group

  2. “The ongoing convergence between Grids, Web Services and the Semantic Web is a fundamental step towards the realisation of a common service-oriented architecture empowering people to create, provide, access and use a variety of intelligent services, anywhere, anytime, in a secure, cost-effective and trustworthy way.” Next Generation Grids 2 Requirements and Options for European Grids Research 2005-2010 and Beyond EU Expert Group Report July 2004

  3. “To realise the Next Generation Grid requires semantically rich information representation, the exploitation of knowledge, and co-ordination and orchestration that is aware of context and task” David Snelling, NextGRID Building Intelligent Grid Services

  4. State properties of a resource Data in a purchase order Current usage agreement for resources on a grid Metrics associated with work load or performance on a Web server Declarative descriptions of data sets, codes, services, workflows Typing and classifying service or workflow inputs, outputs, goals, … Access rights to resources Declarative descriptions for, and records of, service interactions event notification topics, provenance trails, monitoring records Policy and profile encoding personal profiles and security groupings Used in job control; workflow composition, semantic dataset integration, resource brokering, resource scheduling, problem solving selection, intelligent portals… GGF WG-CMM, CIM, GIS, MDS, …. Knowledge everywhere already…its called metadata

  5. Knowledge and the knowledge producing & consuming protocols & patterns are already in Grid Middleware and Grid Applications. Embedded in middleware code, in schemas, in catalogues, in applications and in practice.

  6. Bringing knowledge into the light Managing and operating a Grid intelligently requires: 1. Knowledge • Knowledge about the state and properties of Grid components, and their configurations • Mechanisms for interpreting that knowledge 2. Intelligently acquiring and refreshing knowledge 3. Use it practically in decision making.

  7. Web Services Developers Semantic Web Services Grid services Semantic Web and Agents Grid Semantic Grid Aesthetics Plumbers Theoreticians Engineers Semantics for the Grid Grid services for Semantic Web Convergence • Semantic Web Technologies • Semantic Web itself

  8. Semantic Web mechanisms Trust p -> a; p=a p -> a; p=a Rules SWRL • Uniform naming scheme. • Metadata – descriptions of properties and content • Metadata – glue linking resources together • Ontologies – interpretation of metadata for people and processes. p -> a; p=a p -> a; p=a p -> a; p=a Ontologies OWL/RDFS Metadata Annotation RDF Search engines and filters Web XML, URI, UniCode Applications Deep web PHP, WS*

  9. Making Knowledge Explicit RDF Resource Description Framework OWL Web Ontology Language

  10. Ontology Metadata assertion Object Make knowledge explicit. Make knowledge protocols explicit. Describe some of these declaratively so they might be exchanged and machine processed. Metadata data – here is what it is and/or how it relates to something else Ontologies / controlled vocabularies – we understand each other

  11. Scientific Applications Scientists Grid platform and resources Grid Middleware Security policies standards Service Computer Scientists Providers Knowledge Stakeholders Knowledge for Grid Applications Knowledge for the operation of the Grid Sources of Knowledge

  12. knowledge worker'sapplications and tools Grid Domain Applications Upper domain generic services Collective services “Plumbing” Application Knowledge Base services Operational Knowledge System services Web Service Resource Framework Web Service-Notification WS-I+ Web Services

  13. The Semantic Grid is an extension of the current Grid in which information and services are given well-defined and explicitly represented meaning, better enabling computers and people to work in cooperation Semantics in and on the Grid

  14. Time to move beyond slogans.

  15. Semantic Grid roadmap • Exploit the languages from the Semantic Web and other. • Specifying and developing the architectural components and tools forming the infrastructure of the Semantic Grid and define the architecture of the (Semantic) Grid. • Prototyping applications using the languages, the components and defining the content necessary. Developing in parallel, yet are interdependent. A maelstrom of research coupled concurrently with standards activity, and early experiments and prototypes running alongside (some) commercial developments.

  16. CombeChem SDK Semantic Grid trajectory Demonstration Phase Efforts Systematic Investigation Phase Specific experiments Part of the Architecture Dagstuhl Schloss Seminar Grid Resource Ontology Many projects Pioneering Phase Ad-hoc experiments, early pioneers SRB GGF Semantic Grid Research Group Many workshops Implicit Semantics OGSA generation Implicit Semantics 1st generation Time

  17. Three strands Knowledge Aware Grid Services KAGS Grid Compliant Knowledge Services GCKS P4 Semantic (Grid Services) (Semantic Grid) Services Grid Aware Knowledge Services GAKS And how all these services play together Profiles, Protocols, Patterns, Policies

  18. Three strands Knowledge Aware Grid Services KAGS Grid Compliant Knowledge Services GCKS Middleware Knowledge: Additional port types relating to knowledge, for example discovery. Functionality: Existing operations for interaction with a knowledge service Metadata: How fast? What language is supported? Lifetime Management: Factory methods, creation of resources Grid Aware Knowledge Services GAKS Use of Grid infrastructure within the implementation of the service.

  19. Grid Compliant Knowledge Services • Take today’s knowledge services from the Semantic web and other worlds • What does it mean for them to be Grid Services? • What are the state properties of an ontology grid service? • What are the lifetime management properties of an ontology grid service? • What is a virtualised and dynamically provisioned ontology service, (metadata store, metadata annotator, reasoner …) ? • How will an ontology grid service and a metadata grid service play together?

  20. Grid Compliant Ontologies Resource • A distinguishable unique identity and lifetime (usually static) • Maintains a specific state that can be materialized • May be accessed through one or more Web Services • Artifact - a file, XML document, database, usually real (could be virtual). Could be compound. Service • Base interface for inspecting and manipulating an ontology • A well defined “Ask-Tell” API: getSubConcepts(concept), getSuperConcepts(concept), classify, checkSatisfiability(concept), put(conceptExpression) … • Resource – a connection to the Ontology Service • An ontology might be just a file. Or an application. Or embedded in an application after a community has thought about it for a bit.

  21. Ontology as an OGSA-DAI Realization WS-DAI Message Patterns Behavioural Properties Provide a realization of WS-DAI with specific ontology messages (activities) WS-DAIR Relational WS-DAIO Ontology WS-DAIX XML WS-DAIO-RDF RDF Specific WS-DAIO-OWL OWL specific

  22. Jena Sesame RDF Annotation store as an OGSA-DAI Realization WS-DAI Message Patterns Behavioural Properties Provide a realization of WS-DAI for RDF WS-DAIR Relational WS-RDF WS-DAIX XML DB2 mySQL

  23. Data -> Ontology Access • Data Access collects together messages that access and/or modify a resource • Note: the messages are ignorant of the query other than its class. • OSGA-DIAO • The message patterns & the behavioural properties • The API for the ontology querying • The realisation mapping to the ontology language – OWL, RDF, RDFS, DAG

  24. Knowledge Aware Grid Services • Take a Grid service and see how it might take advantage of a knowledge service or knowledge resource. • Might be a base Grid service or an Application Service or a high level Grid service. • What are the generic and specific knowledge services required for Grid? • Two starting points: • Discovery. Registry/Brokering – shared semantics; resource annotation; painless knowledge recovery. • Debugging – shared semantics; knowledge collection; knowledge recovery.

  25. Specific Application Ontology Web Service Web Service Generic Schema for Web (Grid) Services Web Interface API API Semantic Web Services • Semantic Web – describing data • Semantic Web Services – describing processes. • WSMO, OWL-S Thierry’s observations about Web Service abstractions

  26. Discovery in Taverna workflow workbench • Taverna currently ships with access to >1000 publicly available bioinformatics services • Bioinformatican chooses services when forming workflows, with assistance. • A common ontology is used to annotate and query any myGrid object including services. • Discover workflows and services described in the registry via Taverna. • Look for all workflows that accept an input of semantic type “nucleotide sequence”

  27. Metadata discovery Ontology Acquisition Resource discovery Semantic Discovery Low level descriptions WSDL, Scufl Reasoner Feta skeletons generated by mining low level descriptions myGrid domain classification Ontology editor Feta importer Ontologist builds myGrid Domain Ontology Knowledge Engineer PeDRo annotator Feta semantic discovery engine Annotator Descriptions are loaded and engine initiated Search requests Skeletal descriptions are annotated Taverna workbench clients UDDI registry Feta GUI Resource match make KAVE provenance User interacts with GUI to discover resources Annotated descriptions are stored

  28. Intelligent Debugging Architecture Acklin

  29. ..masked_sequence_of .. nucleotide_sequence project ..part_of organisation >gi|19747251|gb|AC005089.3| Homo sapiens BAC clone CTA-315H11 from 7, complete sequence AAGCTTTTCTGGCACTGTTTCCTTCTTCCTGATAACCAGAGAAGGAAAAGATCTCCATTTTACAGATGAG GAAACAGGCTCAGAGAGGTCAAGGCTCTGGCTCAAGGTCACACAGCCTGGGAACGGCAAAGCTGATATTC AAACCCAAGCATCTTGGCTCCAAAGCCCTGGTTTCTGTTCCCACTACTGTCAGTGACCTTGGCAAGCCCT GTCCTCCTCCGGGCTTCACTCTGCACACCTGTAACCTGGGGTTAAATGGGCTCACCTGGACTGTTGAGCG experiment definition rdf:type ..part_of group urn:lsid:taverna:datathing:13 ..part_of ..author workflow definition ..works_for ..invocation_of ..author person ..BLAST_Report workflow invocation ..similar_sequences_to ..run_for ..run_during service description rdf:type 19747251 AC005089.3 831 Homo sapiens BAC clone CTA-315H11 from 7, complete sequence 15145617 AC073846.6 815 Homo sapiens BAC clone RP11-622P13 from 7, complete sequence 15384807 AL365366.20 46.1 Human DNA sequence from clone RP11-553N16 on chromosome 1, complete sequence 7717376 AL163282.2 44.1 Homo sapiens chromosome 21 segment HS21C082 16304790 AL133523.5 44.1 Human chromosome 14 DNA sequence BAC R-775G15 of library RPCI-11 from chromosome 14 of Homo sapiens (Human), complete sequence 34367431 BX648272.1 44.1 Homo sapiens mRNA; cDNA DKFZp686G08119 (from clone DKFZp686G08119) 5629923 AC007298.17 44.1 Homo sapiens 12q22 BAC RPCI11-256L6 (Roswell Park Cancer Institute Human BAC Library) complete sequence 34533695 AK126986.1 44.1 Homo sapiens cDNA FLJ45040 fis, clone BRAWH3020486 20377057 AC069363.10 44.1 Homo sapiens chromosome 17, clone RP11-104J23, complete sequence 4191263 AL031674.1 44.1 Human DNA sequence from clone RP4-715N11 on chromosome 20q13.1-13.2 Contains two putative novel genes, ESTs, STSs and GSSs, complete sequence 17977487 AC093690.5 44.1 Homo sapiens BAC clone RP11-731I19 from 2, complete sequence 17048246 AC012568.7 44.1 Homo sapiens chromosome 15, clone RP11-342M21, complete sequence 14485328 AL355339.7 44.1 Human DNA sequence from clone RP11-461K13 on chromosome 10, complete sequence 5757554 AC007074.2 44.1 Homo sapiens PAC clone RP3-368G6 from X, complete sequence 4176355 AC005509.1 44.1 Homo sapiens chromosome 4 clone B200N5 map 4q25, complete sequence 2829108 AF042090.1 44.1 Homo sapiens chromosome 21q22.3 PAC 171F15, complete sequence urn:lsid:taverna:datathing:15 service invocation ..described_by ..created_by ..filtered_version_of A B Keeping track Relationship BLAST report has with other Other classes of information related to BLAST report Jun Zhao, Chris Wroe, Carole Goble, Robert Stevens, Dennis Quan, Mark Greenwood, Using Semantic Web Technologies for Representing e-Science Provenance in Proc 3rd International Semantic Web Conference, Hiroshima, Japan, Nov 2004

  30. Grid Aware Knowledge Services • What is the architecture of distributed knowledge services? • Can Grid platforms realistically provide a robust distributed stateful computing platform for agent systems? • OGSA-DAIS for RDF repositories. • Replica location service for replicated knowledge services. • Secure file transfer for metadata. • Event notification for metadata or ontology updates. • Authentication and authorisation for updates. • Metadata updated by workflows; • Security and RDF! • Distributed reasoning !! • Depends on the availability of these Grid services.

  31. Subscriber Broker WS-Notification and Semantic Integrity • Subscriber – an Annotation Service - indicates interest in a particular (semantic) topic – Ontology Version change - by issuing a subscribe request • Subscriptions are WS-Resources • Various subscriptions are possible • Notification may be triggered by: • WS Resource Property value changes • Other “situations” • Broker examines current subscriptions • Brokers may • “Transform” or “interpret” topics <- knowledge! subscribe notify Metadata service notify notify subscribe S S S Publisher notify Ontology Service Adapted from Dr. Daniel Sabbah, IBM, Globus World 2004.

  32. Yet Another Stack Car repair settlement, satellite data configuration. Grid Application and Application Services resource discovery, intelligent debugging, provenance mining OGSA OntoKit knowledge Generation services Patterns & Upper Services: Semantic broker, semantic registry, semantic logging, semantic workflow management, vocabulary management PATTERNS OF INTERACTION OGSA OntoKit semantic grid services Base services: annotation management ontology access and integration, annotation access, reasoning, ontology alignment GRID PROPERTIES OGSA plumbing services OGSA OntoKit plumbing services Resources: Ontology, Knowledge Base, Registry, Database DOMAIN & MIDDLEWARE Resources

  33. Obstacles to Overcome • Semantic what? • Compelling use cases • “Revolution is only possible when it becomes inevitable” • Niche activity. • No content or hard to get the content! • Ontology acquisition. Pain-free metadata acquisition. • Baggage of communities • Different agendas • Hendler Principle: “A little semantics goes a long way”. • Failure to mainstream – agents • Instability of both platforms • Middleware hard to use and incomplete • Off putting to “the other side” • Deployment, research, development, applications and standardisation all happening together • Whither Grid Architecture?

  34. Prof.dr. Žiga Turk MDA and the Grid Computation Independent Model • Where is grid? • current grids are on a platform level • grids compatible with service oriented architectures are on ASM level • Challenge: • should grids do better than SOA based on Web Services? • automatic transformation of PIM models into a grid specific ASMs and PSMs • Opportunity: • transform a business level architectures to Web Services, Grid, whatever-comes-next platform manual PlatformIndependent Model automatic ArchitectureSpecific Model e.g. OGSA automatic Platform Specific Model e.g. GT4, gLite semi automatic working system

  35. Map concepts between ontologies • Unicore and GLUE have different philosophies for describing resources :-( • In Unicore, the resources are described in terms of resource requests • In GLUE, resources are described in terms of the availability of resources.

  36. Not all knowledge will use separate services Use Explicit Ontologies Rules Non-embedded metadata Embedded metadata Type systems Schemata Implicit Text descriptions Shared human consensus Implicit Explicit Assertion

  37. Source of metadata and knowledge • Grid Resource Ontology • Activation Energy • Metadata mining • The network effect – service providers rule • Return on investment for service providers and users • Applications keep it real: listen to users to take short cuts.

  38. Semantic proportionsspeculation – no empirical foundation at all Generic Grid Resource Application

  39. Grid Knowledge, Agents & the Semantic Web • Knowledge aware grid services Overcoming community divisions Growing pains of middleware Make it easier not harder or more “interesting” A little semantics goes a long way Evolution not revolution Technology push

  40. “WSRF is the instruction set of the Grid” Thierry Priol Semantic Grid Services Grid service behaviour WSRF WS-I+

  41. Whither Grid Architecture?

  42. K-WfGrid InteliGrids SIMDAT Provenance Applications Use Cases UniGrids SDK Grid Architecture Semantic Architecture NextGRID WSRF WS-I+ Semantic Grid Architecture

  43. Summary • What existing technologies can we harness and what needs to be done that is new? • Semantic SOA – what are the resources, services, profiles, patterns and policies? • What are the appropriate abstractions for a Semantic Grid based architecture? (or a Grid Architecture?) • How will semantics make the Grid more flexible and simpler – and how do we avoid making it more complicated! • How do we ensure close cooperation with design and development of next generation Grid research and next generation knowledge research?

  44. Thanks • myGrid consortium, esp. Phil Lord, Pinar Alper, Chris Wroe, Luc Moreau • OntoGrid project members • Norman Paton, OGSA-DAI • Prof.dr. Žiga Turk, InteliGrids • John Brooke, UniGrids • Stephane Viali • Thierry Pioli, CoreGrid • David de Roure, GGF Sem-Grd RG http://www.semanticgrid.org/

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