Peer-to-Peer Result Dissemination in High-Volume Data Filtering

Data filtering is the problem of matching high-volume document streams, against a set of client profiles, often represented by queries. Recent work in highvolume data filtering, like the YFilter project at Berkeley, has focused on efficiently indexing client queries, by exploiting the similarity between queries. However, the problem of result dissemination in data filtering has not been received enough attention. Each filtered document has to be delivered to a unique, and typically large set of clients, making this a problem of highly dynamic multicast. We observe fundamental limitations in the client-server delivery model for the high-volume data filtering problem and describe a peer-to-peer scheme to solve it. It exploits the bandwidths of participating clients for data dissemination by building an unstructured overlay network. Our deployment on the PlanetLab testbed shows that the approach proposed is scalable and offers acceptable delivery delays and network economy.