Optimized cluster-based filtering algorithm for graph metadata

With the increasing amount of information on the Web and the proliferation of RSS offerings, efficient graph-based metadata filtering algorithm for large scale information dissemination is very important today. Matching graph-based documents is expensive due to the expressiveness of the language. The centralized architecture does not work well for the large scale information dissemination service. To address these problems, in this paper we develop a cluster-based publish/subscribe system for filtering graph-based RSS documents. Essentially, we develop two indexing algorithms to enable workload distribution and cluster-based filtering. Furthermore, we proposed an optimized graph matching algorithm which speeds up the constraint evaluation for subscriptions. The experimental results show that we can support one million subscriptions on a compute cluster with 5-20 nodes and the throughput scales linearly with the number of cluster nodes.