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Scalable ranked publish/subscribe

Machanavajjhala Ashwin, Vee Erik, Garofalakis Minos, Shanmugasundaram, Jayavel

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URIhttp://purl.tuc.gr/dl/dias/17F3ED4B-8998-4731-AA68-59A0ECD195A9-
Identifierhttp://www.vldb.org/pvldb/1/1453906.pdf-
Languageen-
Extent12 pagesen
TitleScalable ranked publish/subscribeen
CreatorMachanavajjhala Ashwinen
CreatorVee Eriken
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorShanmugasundaram, Jayavelen
PublisherAssociation for Computing Machineryen
Content SummaryPublish/subscribe (pub/sub) systems are designed to efficiently match incoming events (e.g., stock quotes) against a set of subscriptions (e.g., trader profiles specifying quotes of interest). However, current pub/sub systems only support a simple binary notion of matching: an event either matches a subscription or it does not; for instance, a stock quote will either match or not match a trader profile. In this paper, we argue that this simple notion of matching is inadequate for many applications where only the “best” matching subscriptions are of interest. For instance, in targeted Web advertising, an incoming user (“event”) may match several different advertiser-specified user profiles (“subscriptions”), but given the limited advertising real-estate, we want to quickly discover the best (e.g., most relevant) ads to display. To address this need, we initiate a study of ranked pub/sub systems. We focus on the case where subscriptions correspond to interval ranges (e.g, age in [25,35] and salary > $50, 000), and events are points that match all the intervals that they stab (e.g., age=28, salary = $65,000). In addition, each interval has a score and our goal is to quickly recover the top-scoring matching subscriptions. Unfortunately, adapting existing index structures to solve this problem results in either an unacceptable space overhead or a significant performance degradation. We thus propose two novel index structures that are both compact and efficient. Our experimental evaluation shows that the proposed structures provide a scalable basis for designing ranked pub/sub systems. en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-30-
Date of Publication2008-
SubjectDatabase managementen
SubjectWeb advertisingen
Bibliographic CitationA. Machanavajjhala, E. Vee, M. Garofalakis and J. Shanmugasundaram, "Scalable ranked publish/subscribe", in the 34th International Conference on Very Large Data Bases, 2008, pp. 451-462.en

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