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  • Assumption 1: lots of learning resource repositories, which typically employ various back-ends, various meta-data schemas, and various architectures, etc., have already existed in many institutions . ( troubles: isolate information island s , lack of interoperability between each other ) Assumption 2: Many institutions are reluctant to give up their control over learning resources, which is currently troubling many central-server based approaches to learning resource sharing, e.g., eLearning „ portals“. (troubles: „ p ortals“ are costly but un profitable )
  • Semantic Web Peer-to-Peer (P2P) Edutella Query Service OAI-P2P: a P2P network for Open Archives

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  • 1. Super-Peer-Based Routing and Clustering Strategies for RDF-Based Peer-To-Peer Networks Alexander Löser Technische Universität Berlin, Germany Wolfgang Nejdl, Martin Wolpers, Wolf Siberski , Christoph Schmitz, Mario Schlosser, Ingo Brunkhorst Learning Lab Lower Saxony, Hannover/Karlsruhe, Germany
  • 2. Overview
    • Introduction to Edutella
    • Schema-based P2P systems
    • Super-Peer networks
    • The HyperCuP topology
    • Indexing
    • Routing
    • Clustering
    • Schema Mapping
    • Further Work
  • 3. PADLR: Personalized Access to Digital Learning Resources
      • Heterogeneous
        • Applications
        • Repositories
        • Platforms
  • 4. Edutella: Introduction
    • Main Goal: Achieve interoperability between heterogeneous metadata-driven (e-learning) systems
    • Provides metadata only, not the resources
      • resources are fetched via http
    • Foundations
      • Semantic Web
      • Peer-to-Peer
      • Federated Databases
    • Open source project ( http:// edutella . jxta .org )
    • Uses other OSS: JXTA Platform, Jena, JUnit, Ant
      • Uses: Xerces, Jetty, ICU4J, XIndice, ...
  • 5. Query Service
    • provides standardized query/retrieval of RDF metadata stored in distributed RDF repositories
    • Query Exchange Language
      • Based on Datalog (allows expression of rules)
      • RDF syntax
      • For exchange only
    • Adapters to enable QEL query processing on several backends:
      • File, RDBMS, Rule Database, ...
  • 6. Schema-Based Peer-to-Peer Networks
    • User-definable schemas
    • Structured schemas
    • Query language
    (system list not complete)
    • No central control
    • Node autonomy
    • Self organization
  • 7. Problem and Approach
    • Broadcasting all queries to all information sources obviously doesn‘t scale
    • Problem: How to distribute queries in a scalable fashion?
    • Optimal solution: distribute a query only to peers which have results for it
    • Approach
      • Use Super-Peer network
      • Introduce Query Routing Indices
  • 8. Super-Peer Networks
    • Observation: Peers vary significantly in availability, bandwith, processing power, etc.
    • Create network backbone from highly available and powerful peers to distribute load better.
  • 9. Super-Peer Topology
    • Super-peers are arranged as HyperCuP
    • Broadcast needs n-1 messages, log 2 (n) hops
    • High connectivity, resilient against node failures
  • 10. Routing Indices
    • On joining the network, each peer provides self-description
    • Based on this information, super-peers maintain indexes of schemas/schema elements used at each peer
    • Super-peer/peer indices
    • Super-peer/super-peer indices
    • Index Granularity
      • Schema
      • Property
      • Property + value range
      • Property + individual values
  • 11. Index Sample ... dc SP 1 , SP 3 ,SP 4 lom SP 1 ,SP 4 SP 2 lom:context P 1 dc:subject ccs:sw-eng P 1 dc:language „de“ P 1 ... SP 1
  • 12. Query Routing Sample Find any resource with dc:subject=ccs:sw-eng and lom:context=“undergrad”
  • 13. Clustering
    • If peers are randomly assigned to super-peers, we often still have to broadcast queries within the super-peer network
    • Two approaches:
      • Static: super-peer administrators define constraints which peers have to fulfill to be accepted
      • Dynamic: based on query statistics, peers are continually reassigned to optimize query distribution
    • Work in progress
  • 14. Schema Mapping
    • Peers may use different schemas to annotate their resources
    • Use federated database techniques for mapping
      • Super-peers acts as Mediators
      • Mapping rules have to be specified manually
  • 15. Super-peer/Peer implementation Peer Super-Peer
  • 16. Further Work
    • Quantitative evaluation (by simulation)
    • Exploration of clustering approaches
    • Integration of other mediation techniques
  • 17. The End