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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 present a query-driven algorithm for the distributed indexing of large document collections within structured P2P networks. To cope with bandwidth consumption that has been identified as the major problem for the standard P2P approach with single term indexing, we leverage a distributed index that stores up to top-k document references only for carefully chosen indexing term combinations. In addition, since the number of possible term combinations extracted from a document collection can be very large, we propose to use query statistics to index only such combinations that are indeed frequently requested by the users. Thus, by avoiding the maintenance of superfluous indexing information, we achieve a substantial reduction in bandwidth and storage. A specific activation mechanism is applied to continuously update the indexing information according to changes in the query distribution, resulting in an efficient, constantly evolving query-driven indexing structure. We show that the size of the index and the generated indexing/retrieval traffic remains manageable even for websize document collections at the price of a marginal loss in precision for rare queries. Our theoretical analysis and experimental results provide convincing evidence about the feasibility of the query-driven indexing strategy for large scale P2P text retrieval. Moreover, our experiments confirm that the retrieval performance is only slightly lower than the one obtained with state-of-the-art centralized query engines.

Peer-to-peer networks have been identified as promising architectural concept for developing search scenarios across digital library collections. Digital libraries typically offer sophisticated search over their local content, however, search methods involving a network of such stand-alone components are currently quite limited. We present an architecture for highly-efficient search over digital library collections based on structured P2P networks. As the standard single-term indexing strategy faces significant scalability limitations in distributed environments, we propose a novel indexing strategy-key-based indexing. The keys are term sets that appear in a restricted number of collection documents. Thus, they are discriminative with respect to the global document collection, and ensure scalable search costs. Moreover, key-based indexing computes posting list joins during indexing time, which significantly improves query performance. As search efficient solutions usually imply costly indexing procedures, we present experimental results that show acceptable indexing costs while the retrieval performance is comparable to the standard centralized solutions with TF-IDF ranking.

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.