1. VLDB2005 CMS-ToPSS: Efficient Dissemination of RSS Documents Milenko Petrovic Haifeng Liu Hans-Arno Jacobsen University of Toronto

2. VLDB05 2 Information Dissemination Easy to use web publishing tools (blog, wiki) are fueling the increase in the number of web publishers RSS frequently used to disseminate update to interested users  CNN.com, Yahoo! News, Amazon.com, MSN search (beta) RSS aggregator RSS readers RSS publishers Problem: Polling based architecture

3. VLDB05 3 Solution! Current rss dissemination architecture G-ToPSS rss dissemination architecture  

4. VLDB05 4 Interaction Model: Publish/Subscribe Broker Publisher Subscriber RSS feeds Matching RSS feeds Queries over all RSS

5. VLDB05 5 Research challenges 1. Need a subscription (query) language suitable for filtering of rss documents 2. Need an efficient matching algorithm based on graph representation Structurally matching Constraint matching 3. Scalability to a large number of subscriptions and high publishing rate

6. VLDB05 6 CMS-ToPSS System Architecture

7. VLDB05 7 Subscription Scalability

8. VLDB05 8 Memory Scalability

9. VLDB05 9 Matching Semantics PAPER17 “Arno Jacobsen” AUTHOR SIGMOD CONFERENCE “California” LOCATION “2001” YEAR ?y (?y <= Publication) “Arno Jacobsen” AUTHOR SIGMOD CONFERENCE ?z (?z > 2000) YEAR Publication Subscription

10. VLDB05 10 Data Model (RSS Documents) Publications are represented as directed graphs with node and edge labels Node labels are typed  Literal value  Class Edge labels are typed  Class Classes can be related using multiple inheritance ontology

11. VLDB05 11 Query Language (GQL) Queries are represented as directed graph patterns with node and edge labels Node labels are variables Variables can be constrained by  Classes  Class instances and literal values Edge labels are class instances Mapping (matching) semantics  Pattern graph maps to data graph if the topology (structure) of the two graphs matches and all variable constraints are satisfied

12. VLDB05 12 Conclusion and Future Work Proposed a prototype for graph-based metadata filtering G-ToPSS supports high matching rate for an expressive subscription language Extend G-ToPSS with full RDF language features Optimize constraint processing during matching