Searching dynamic communities with personal indexes

Abstract

Often the challenge of finding relevant information is reduced to find the 'right' people who will answer our question. In this paper we present innovative algorithms called INGA (Interest-based Node Grouping Algorithms) which integrate personal routing indices into semantic query processing to boost performance. Similar to social networks peers in INGA cooperate to efficiently route queries for documents along adaptive shortcut-based overlays using only local, but semantically well chosen information. We propose active and passive shortcut creation strategies for index building and a novel algorithm to select the most promising content providers depending on each peer index with respect to the individual query. We quantify the benefit of our indexing strategy by extensive performance experiments in the SWAP simulation infrastructure. While obtaining high recall values compared to other state-of-the-art algorithms, we show that INGA improves recall and reduces the number of messages significantly. © Springer-Verlag Berlin Heidelberg 2005.