Results caching is an efficient technique for reducing the query processing load, hence it is commonly used in real search engines. This technique, however, bounds the maximum hit rate due to the large fraction of singleton queries, which is an important limitation. In this paper we propose ResIn - an architecture that uses a combination of results caching and index pruning to overcome this limitation.

In this paper we present the AlvisP2P IR engine, which enables efficient retrieval with multi-keyword queries from a global document collection available in a P2P network. In such a network, each peer publishes its local index and invests a part of its local computing resources (storage, CPU, bandwidth) to maintain a fraction of a global P2P index. This investment is rewarded by the network-wide accessibility of the local documents via the global search facility. The AlvisP2P engine uses an optimized overlay network and relies on novel indexing/retrieval mechanisms that ensure low bandwidth consumption, thus enabling unlimited network growth. Our demonstration shows how an easy-to-install AlvisP2P client can be used to join an existing P2P network, index local (text or even multimedia) documents with collection-specific indexing mechanisms, and control access rights to them.

In this paper, we present a query-driven indexing/retrieval strategy for efficient full text retrieval from large document collections distributed within a structured P2P network. Our indexing strategy is based on two important properties: (1) the generated distributed index stores posting lists for carefully chosen indexing term combinations, and (2) the posting lists containing too many document references are truncated to a bounded number of their top-ranked elements. These two properties guarantee acceptable storage and bandwidth requirements, essentially because the number of indexing term combinations remains scalable and the transmitted posting lists never exceed a constant size. However, as the number of generated term combinations can still become quite large, we also use term statistics extracted from available query logs to index only such combinations that are frequently present in user queries. Thus, by avoiding the generation of superfluous indexing term combinations, we achieve an additional substantial reduction in bandwidth and storage consumption. As a result, the generated distributed index corresponds to a constantly evolving query-driven indexing structure that efficiently follows current information needs of the users. More precisely, our theoretical analysis and experimental results indicate that, at the price of a marginal loss in retrieval quality for rare queries, the generated index size and network traffic remain manageable even for web-size document collections. Furthermore, our experiments show that at the same time the achieved retrieval quality is fully comparable to the one obtained with a state-of-the-art centralized query engine.

We describe a query-driven indexing framework for scalable text retrieval over structured P2P networks. To cope with the bandwidth consumption problem that has been identified as the major obstacle for full-text retrieval in P2P networks, we truncate posting lists associated with indexing features to a constant size storing only top-k ranked document references. To compensate for the loss of information caused by the truncation, we extend the set of indexing features with carefully chosen term sets. Indexing term sets are selected based on the query statistics extracted from query logs, thus we index only such combinations that are a) frequently present in user queries and b) non-redundant w.r.t the rest of the index. The distributed index is compact and efficient as it constantly evolves adapting to the current query popularity distribution. Moreover, it is possible to control the tradeoff between the storage/bandwidth requirements and the quality of query answering by tuning the indexing parameters. Our theoretical analysis and experimental results indicate that we can indeed achieve scalable P2P text retrieval for very large document collections and deliver good retrieval performance.