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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
  • www2003_8.ppt

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