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Sven Bittner, 12 April 2007

Talk at the 5th New Zealand Computer Science Research Student Conference NEWS ALERT: (Kiwi or Cow) and Chainsaw = (Kiwi and Chainsaw) or (Cow and Chainsaw)? YEAH RIGHT. Sven Bittner, 12 April 2007. Structure of Talk. Motivation & Problem Undertaken Research Filtering Event Routing

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Sven Bittner, 12 April 2007

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  1. Talk at the 5th New Zealand Computer Science Research Student ConferenceNEWS ALERT:(Kiwi or Cow) and Chainsaw = (Kiwi and Chainsaw) or (Cow and Chainsaw)?YEAH RIGHT... Sven Bittner, 12 April 2007

  2. Structure of Talk • Motivation & Problem • Undertaken Research • Filtering • Event Routing • Advertisements • Current Steps & Summary Sven Bittner – Expressive Subscriptions and Advertisements in Pub/Sub Systems

  3. Structure of Talk • Motivation & Problem • Undertaken Research • Filtering • Event Routing • Advertisements • Current Steps & Summary Sven Bittner – Expressive Subscriptions and Advertisements in Pub/Sub Systems

  4. Problem: Information Overflow • More and more electronically available information • Users only want information they are interestedin Motivation Research Filtering Routing Advertisements Summary

  5. Solution: Publish/Subscribe Filtering of information (event messages)according to user interests (subscriptions) Incoming information Information of interest Motivation Research Filtering Routing Advertisements Summary

  6. Advertise future event messages Register subscriptions Publish event messages Sends notifications Event routing table Event routing table … … … … Advertisem. and subscript. index structures Filtering and routing … … … … Subs. routing table Subs. routing table … … … … Pub/Sub Systems: Details Pub/sub system Publishers Subscribers B4 B3 B5 B6 B2 B1 B7 B8 B9 Motivation Research Filtering Routing Advertisements Summary

  7. Problem/Hypothesis • Focus on conjunctive subscr./advert. • Argument: Boolean forms can be converted to DNF • DNF exponential in size • Already many subscr./advert. without conversion • Hypothesis [B06] • Direct support of Boolean form decreasesmemory usage without degradingefficiency Original: (Kiwi or Cow) and Chainsaw DNF: (Kiwi and Chainsaw) or (Cow and Chainsaw) Motivation Research Filtering Routing Advertisements Summary

  8. Structure of Talk • Motivation & Problem • Undertaken Research • Filtering • Event Routing • Advertisements • Current Steps & Summary Sven Bittner – Expressive Subscriptions and Advertisements in Pub/Sub Systems

  9. Three Research Areas 1. Centralfiltering of arbitrary Boolean subscriptions 2. Event routing optimizations for arbitrary Boolean subscriptions 3. Support of arbitrary Boolean advertisements a) Calculation of overlappings b) Subscription routing optimization Motivation Research Filtering Routing Advertisements Summary

  10. Candidate subscription matching Real subscription matching Predicate matching Matching predicates Candidate subscriptions Incoming event Matching subscriptions Predicate indexes Subscription indexes Subscriptions Central Filtering Algorithm (1) • Utilization of one-dimensional indexes • Extension of conjunctive counting approach • Three-step filtering [BH05a] Motivation Research Filtering Routing Advertisements Summary

  11. Central Filtering Algorithm (2) • Evaluation [BH05b] • Memory requirements • Development of characterizationscheme • Theoretical analysis based on scheme • Practicalverification • Efficiency • Empirical experiments, similar/better results  Proves hypothesis for central components Motivation Research Filtering Routing Advertisements Summary

  12. Structure of Talk • Motivation & Problem • Undertaken Research • Filtering • Event Routing • Advertisements • Current Steps & Summary Sven Bittner – Expressive Subscriptions and Advertisements in Pub/Sub Systems

  13. Event Routing Optimization (1) • Subscription pruning [BH06a] • Applicable to all kinds of subscriptions • Tailored for various targetparameters (me-mory usage, filter efficiency, network load) [BH06c] • Optimization idea • Broadening of subscriptions by pruning • Noeffect on filtering accuracy (only internal) Reduction of complexity of routing tableentries Motivation Research Filtering Routing Advertisements Summary

  14. Selected pruning on selected entries Optimized routing table Subscription … Neighbor N1 N2 N3… Event Routing Optimization (2) Un-optimized routing table Subscription … Neighbor N1 N2 N3… Motivation Research Filtering Routing Advertisements Summary

  15. Event Routing Optimization (3) • Analysis (empirical experiments) • Strong reduction in table size (e.g., by 80%) • Strong increase in throughput (e.g., by 50%)  Promising optimization effect • Comparison to covering optimization • Stable optimization behavior of pruning • Applicable if other optimizations fail (both subscription structure and relationships)  Proves hypothesis for distributed setting Motivation Research Filtering Routing Advertisements Summary

  16. Structure of Talk • Motivation & Problem • Undertaken Research • Filtering • Event Routing • Advertisements • Current Steps & Summary Sven Bittner – Expressive Subscriptions and Advertisements in Pub/Sub Systems

  17. Support of Advertisements (1) • Calculation of overlappings (two directions) • Overlapping subscriptions: all subscriptions that potentiallymatch messages described by advertisement • Similar to matching algorithm, threesteps • Disjoint predicate matching • Candidate overlapping subscription matching • Real overlapping subscription matching Motivation Research Filtering Routing Advertisements Summary

  18. Support of Advertisements (2) • Calculation of overlappings (two directions) • Evaluation and comparison [BH06b] • Similar efficiency for function problem (all overlappings) • Higher performance (e.g., 85% more efficient) for decision problem (at least one overlapping) Motivation Research Filtering Routing Advertisements Summary

  19. Support of Advertisements (3) • Advertisement-based optimization • Advertisement pruning [BH06d] • First designated subscription routing optimization • Pruning of advertisements • Target parameter: minimalincrease of overlappings when pruning • Evaluation • Proposed measure fulfils design goal  Proves hypothesis for advertisements Motivation Research Filtering Routing Advertisements Summary

  20. Structure of Talk • Motivation & Problem • Undertaken Research • Filtering • Event Routing • Advertisements • Current Steps & Summary Sven Bittner – Expressive Subscriptions and Advertisements in Pub/Sub Systems

  21. Current Steps • Detailed experimental study • Writing up • Redrafting • Redrafting… Motivation Research Filtering Routing Advertisements Summary

  22. Summary • Claim Pub/sub systems should not convert subscriptions and advertisements to DNF for time and space efficiency reasons • Proof • Filtering algorithm (central system) • Event routing optimization (distributed system) • Support for advertisements (if used) Motivation Research Filtering Routing Advertisements Summary

  23. Conclusions • Pub/sub systems should directly work on Boolean expressions (as opposed to DBMSs) (Kiwi or Cow) and Chainsaw = (Kiwi and Chainsaw) or (Cow and Chainsaw)?  • Not the same in pub/sub, because Boolean form • More spaceefficient • More timeefficient Motivation Research Filtering Routing Advertisements Summary

  24. Thank you for your attention! Selected further reading: [BH05a]S. Bittner and A. Hinze. On the Benefits of Non-Canonical Filtering in Publish/Subscribe Systems. In Proceedings of the 25th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW '05), Columbus, USA, June 2005. [BH05b]S. Bittner and A. Hinze. A Detailed Investigation of Memory Requirements for Publish/Subscribe Filtering Algorithms. In Proceedings of the 13th International Conference on Cooperative Information Systems (CoopIS 2005), Agia Napa, Cyprus, 31 October-4 November, 2005. [BH06a] S. Bittner and A. Hinze. Pruning Subscriptions in Distributed Publish/Subscribe Systems. In Proc. of the 29th Australasian Computer Science Conference (ACSC 2006), Hobart, Australia, 16-19 January, 2006. [BH06b] S. Bittner and A. Hinze. Arbitrary Boolean Advertisements: The Final Step in Supporting the Boolean Pub/Sub Model. Technical Report 06/2006. Computer Science Department, Waikato University, June 2006. [BH06c]S. Bittner and A. Hinze. Dimension-Based Subscription Pruning for Publish/Subscribe Systems. In Proceedings of the 26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW '06), Lisbon, Portugal, July 2006. [BH06d]S. Bittner and A. Hinze. Optimizing Pub/Sub Systems by Advertisement Pruning. In Proceedings of the 8th International Symposium on Distributed Objects and Applications (DOA 2006), Montpellier, France, 30 October-1 November 2006. [B06] S. Bittner. Supporting Arbitrary Boolean Subscriptions in Distributed Pub/Sub Systems. In Proceedings of the 3rd Intern. Middleware Doctoral Symposium (MDS 2006), Australia, November 2006. Sven Bittner, s.bittner@cs.waikato.ac.nz Talk: Expressive Subscriptions and Advertisements in Pub/Sub Systems

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