University of Toledo
The amount of content served on social networks can overwhelm users, who must sift through the data for relevant information. To facilitate users, the authors 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. Their 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 their solution.