Distributed ranked data dissemination in social networks

Kaiwen Zhang, Mohammad Sadoghi, Vinod Muthusamy, Hans Arno Jacobsen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

The amount of content served on social networks can overwhelm users, who must sift through the data for relevant information. To facilitate users, we develop and implement dissemination of ranked data in social networks. Although top-k computation can be performed centrally at the user, the size of the event stream can constitute a significant bottleneck. Our approach distributes the top-k computation on an overlay network to reduce the number of events flowing through. Experiments performed using real Twitter and Facebook datasets with 5K and 30K query subscriptions demonstrate that social workloads exhibit properties that are advantageous for our solution.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE 33rd International Conference on Distributed Computing Systems, ICDCS 2013
Pages369-379
Number of pages11
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE 33rd International Conference on Distributed Computing Systems, ICDCS 2013 - Philadelphia, PA, United States
Duration: 8 Jul 201311 Jul 2013

Publication series

NameProceedings - International Conference on Distributed Computing Systems

Conference

Conference2013 IEEE 33rd International Conference on Distributed Computing Systems, ICDCS 2013
Country/TerritoryUnited States
CityPhiladelphia, PA
Period8/07/1311/07/13

Keywords

  • data dissemination
  • publish/subscribe
  • social networks
  • top-k

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