1. Service Discovery and Semantic Overlay Network Creation in DBGlobe University of Ioannina 4th DBGlobe Meeting Paris, June 23, 2003

2. 2Outline zPart 1: Service Discovery zPart 2: Semantic Overlay Networks zPart 3: Ontologies

3. 3Outline zPart 1: Service Discovery zPart 2: Semantic Overlay Networks zPart 3: Ontologies

4. 4 System Architecture zPMOs are attached to Cell Administration Servers (CASs) zCASs are responsible for the service discovery process zXML data or XML-based service descriptions

5. 5 Service Discovery zEach CAS maintains data summaries (e.g. Bloom Filters) to assist query routing A B C SumB SumC

6. 6 Multi-level Bloom Filters zHash-based indices that extend Bloom filters to support the evaluation of path queries. zTwo approaches: Breadth and Depth Bloom Filters that rely on different ways of hashing an XML-tree. zCompact structures zAppearance of false positives

7. 7 Performance Results zMulti-level Bloom filters outperform in terms of false postives Simple Bloom filters in evaluating path queries. zFor 2% of the total size of the data, multi- level Bloom filters evaluate path queries for a false positives ratio below 3% zBreadth Blooms work better than Depth Blooms. zDepth Blooms require more space but are suitable for special type of queries.

8. 8 Filters Distribution zPeers organized into hierarchies connected through a main channel zEach server maintains: ya local filter ya merged filter of the filters in its sub-tree. yIf it is a root-peer(connected to the main channel) a merged filter for every other root-peer

9. 9 Distribution: Hierarchical Organization Node C: Local filter Merged filter :E  F  G  H Root filters: A, B, D

10. 10Outline zPart 1: Service Discovery zPart 2: Semantic Overlay Networks zPart 3: Ontologies

11. 11 Content-based organization zGroup peers together according to their content zUse filter and not data similarity for efficiency zWhen a peer joins the system: it broadcasts its local summary and attaches to the most «similar» peer available

12. 12 Bloom Filter Similarity zNodes organized according to Bloom Filter Similarity zMeasure: similarity measure based on the Manhattan distance metric. Let two filters B and C of size m d(B, C) = |B[1] – C[1]| + |B[2] – C[2]| + … |B[m] – C[m]|. similarity(B, C) = m – d(B, C).

13. 13 Bloom Filter Similarity (cont’d) 1 011 0 001 110 0 100 1 C B similarity(B, C) =8 - (0 + 1 + 1 + 0 + 1 + 0 + 1 + 0) = 4 For multi-level Bloom filters similarity is defined as the sum of each pair of corresponding levels

14. 14 Performance Results zThe content-based organization is much more efficient in finding all the results for a query, than the proximity organization. z They both perform similarly in discovering the first result. z The content-based organization outperforms the proximity one when the nodes that satisfy a given query are limited.

15. 15 Current work zA peer can belong to more than one hierarchies. zSelf-organization--tuning of the system predefined threshold. zService Discovery: yLocate the right cluster (hierarchy) yFind the peer in the hierarchy

16. 16Outline zPart 1: Service Discovery zPart 2: Semantic Overlay Networks zPart 3: Ontologies

17. 17 Ontologies Ontologies are hierarchies --> Thus they can be summarized by multi-level Blooms

18. 18 Ontologies (cont’d) zThe main issue: How to locate the matching hierarchy(cluster) zJust check every root peer. Can we use a global ontology to route us to the matching hierarchy more efficiently?

19. 19 Thank you