1. Management of Uncertainty in Publish/Subscribe Systems Haifeng Liu Department of Computer Sceince University of Toronto

2. Publish/Subscribe Model Publisher Subscriber Notification Stock markets NYSE NASDAQ TSX Subscriptions: IBM > 85 ORCL < 10 JNJ > 60 IBM=84 MSFT=27 INTC=19 JNJ=58 ORCL=12 HON=24 AMGN=58 Publications Subscriptions Broker Network

3. Applications Enabled by Publish/Subscribe Selective information dissemination Information Filtering on the Internet Location-based services Workflow management Intra-enterprise process automation Logistics and supply chain management Enterprise application integration Network monitoring and (distributed) system management

4. Types of Uncertainties Lack of information –Buy a cheap car Imprecision –Sensor data: temperature 15~20ºC, –Location: location (x,y)  location t+1 (x’,y’) Semantics –Synonyms: vehicle vs. automobile –Class taxonomy: CD player vs. electronics –Different expression: 5 years experience vs. graduated in 2001 Problem: manage uncertainties, imprecision and semantics in publish/subscribe system

5. Agenda Distributed Publish/Subscribe Model and Content-based Routing Uncertainties in Publish/Subscribe Research Challenges Approximate P/S Model Graph-structured Model Current Status Research Plan

6. Publish/Subscribe Messages Advertisement (ad) –publication patterns used by publishers to announce the set of publications they are going to publish –E.g. { (stock, any), (price, any) } Subscription (sub) –User interest specification –E.g. (stock = “yahoo”) & ( price ≤ $35) Publication (pub) –Information, data, event –E.g. { (stock, “yahoo”), (price, $32.79) }

7. Content-based Routing Advertising … Distributed Overlay Broker Network … Advertisement *Adopted from SIENA, Gryphon, REBECA and Hermes

8. Content-based Routing Subscribing … Distributed Overlay Broker Network … Subscription *Adopted from SIENA, Gryphon, REBECA and Hermes

9. Content-based Routing Publishing … Distributed Overlay Broker Network … Publication *Adopted from SIENA, Gryphon, REBECA and Hermes

10. Subscription Forwarding I Covering optimization … Distributed Overlay Broker Network … S1: (car=Honda) & (price <= $30K) S2: (car=Honda) & (price <= $25K) S1 covers S2 s1 *Adopted from SIENA, Gryphon, REBECA and Hermes S2 P: {(car = Honda), (price,$20K)}

11. Subscription Forwarding II Merging optimization … Distributed Overlay Broker Network … *Adopted from SIENA, Gryphon, REBECA and Hermes S1 S2 S’ S1: (car=Honda) & (price ≤ $30K) S2: (car=Toyota) & (price ≤ $25K) S’ : (car = any) & (price ≤ $30K) P: {(car = Honda), (price,$20K)}

12. Publish/Subscribe Router Forwarding of advertisements –Via flooding Forwarding of subscriptions –Forward along reverse ad path Matching of ad and sub (Intersecting) –Optimizations Covering/merging of subs Forwarding of publications –Forward along reverse sub path Matching of sub and pub

13. Uncertainties in Distributed Publish/Subscribe System Messages –uncertain subscription –uncertain publication Relations –Between sub and pub –Between sub and sub Result –Return top K matches } representation: modeling } computation: Matching Covering Merging } aggregation: ranking

14. Research Challenges Develop a publish/subscribe model to express uncertainties/semantics in publications and subscriptions Model approximate matching and semantic matching Model approximate covering/merging and semantic covering/merging Scalability to large number of subscribers and high publishing rate

15. Model –Sub: fuzzy set –Pub: possibility distribution Matching –Possibility measure –Necessity measure Ranking –“min” or “product” for conjunction –“max” or “plus” for disjunction Approximate Matching Model

16. Graph-structured Model Model –Pub: directed graph –Sub: directed graph pattern –Semantic: ontology Matching –Pattern graph maps to data graph if the topology (structure) of the two graphs matches and all variable constraints (literal and ontology) are satisfied Ranking PAPER17 Publication Academic Publication Jacobsen’s Publications Report Proceedings WWW VLDB PAPER17 “Arno Jacobsen” AUTHOR CONFERENCE SIGMOD “California” LOCATION “2001” YEAR

17. Current Status Work to date –Develop an approximate p/s model to express uncertainties and an efficient algorithm to do approximate matching –Develop a covering and merging optimizations for approximate content-based routing –Develop a graph-based p/s architecture applied to the dissemination of RDF metadata (including RSS) –Develop two novel algorithms (covering and merging) for creation of a distributed content-based routing network for graph-structured data.

18. Comments from Previous Meeting Probability model Qualitative similarity measure Validate our results –Real data set –Interactive evaluation

19. Research Plan I Membership Function Mining –Get a real data set –“Learn” the membership function Clustering: K-means, DBscan Regression: neural network Semantic Matching and Routing Computation –Matching on ontology –Covering on ontology –Merging on ontology

20. Research Plan II Design an experiment to validate the mining results Design a method to combine possibility measure and necessity measure for ranking Push thresholds down the matching plan to increase the efficiency of matching algorithm Use probabilities as an alternative to model uncertainties and imprecision