STAIRS: Towards Efficient Full-Text Filtering and Dissemination in a DHT Environment

Nowadays contents in Internet like weblogs, wikipedia and news sites become "live". How to notify and provide users with the relevant contents becomes a challenge. Unlike conventional Web search technology or the RSS feed, this paper envisions a personalized full-text content filtering and dissemination system in a highly distributed environment such as a Distributed Hash Table (DHT). Users can subscribe to their interested contents by specifying some terms and threshold values for filtering. Then, published contents will be disseminated to the associated subscribers. We propose a novel and simple framework of filter registration and content publication, STAIRS. By the new framework, we propose three algorithms (default forwarding, dynamic forwarding and adaptive forwarding) to reduce the forwarding cost and false dismissal rate; meanwhile, the subscriber can receive the desired contents with no duplicates. In particular, the adaptive forwarding utilizes the filter information to significantly reduce the forwarding cost. Experiments based on two real query logs and two real datasets show the effectiveness of our proposed framework.