Datenschutzerklärung|Data Privacy

A. Borusan

22.04.2016, 16 Uhr c.t. TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin: "Distributed, Expressive Top-k Subscription Filtering using Covering in Publish/Subscribe Systems" (Dr. Kaiwen Zhang, TU M√ľnchen)

Top-k filtering is an effective way of reducing the amount of
data sent to subscribers in pub/sub applications. We focus on the problem of top-k subscription filtering, where a publication is delivered only to the k best ranked subscribers. The naive approach to perform filtering early at the publisher edge broker works only if complete knowledge of the subscriptions is available, which is not compatible with the well-established covering optimization in content-based publish/subscribe systems. We propose an efficient rank-cover technique to reconcile top-k subscription filtering with covering. We extend the covering model to support topk and describe a novel algorithm for forwarding subscriptions
to publishers while maintaining correctness. We also establish a framework for supporting different types of ranking semantics, and propose an implementation to support fairness. Finally, we conduct an experimental evaluation and perform sensitivity analysis to demonstrate that our optimized rank-cover algorithm retains both covering and fairness while achieving properties advantageous to our targeted workloads. Our optimized solution is scalable and retains over 95% of the covering benefit when k is set at 1% selectivity, and even achieves 70% covering when k selectivity is 10%.