One important problem in peer-to-peer (P2P) networks is searching and retrieving the correct information. However, existing searching mechanisms in pure peer-to-peer networks are inefficient due to the decentralized nature of such networks. We propose two mechanisms for information retrieval in pure peer-to-peer networks. The first, the modified Breadth-First-Search (BFS) mechanism, is an extension of the current Gnuttela protocol, allows searching with keywords, and is designed to minimize the number of messages that are needed to search the network. The second, the Intelligent Search mechanism, uses the past behavior of the P2P network to further improve the scalability of the search procedure. In this algorithm, each peer autonomously decides which of its peers are most likely to answer a given query. The algorithm is entirely distributed, and therefore scales well with the size of the network. We implemented our mechanisms as middleware platforms. To show the advantages of our mechanisms we present experimental results using the middleware implementation.

Webcams, microphones, pressure gauges and other sensors provide exciting new opportunities for querying and monitoring the physical world. In this paper we focus on querying wide area sensor databases, containing (XML) data derived from sensors spread over tens to thousands of miles. We present the first scalable system for executing XPATH queries on such databases. The system maintains the logical view of the data as a single XML document, while physically the data is fragmented across any number of host nodes. For scalability, sensor data is stored close to the sensors, but can be cached elsewhere as dictated by the queries (auto-tuning). Our design enables self-starting distributed queries that jump directly to the lowest common ancestor of the query result, dramatically reducing query response times. We present a novel query-evaluategather technique (using XSLT) for detecting (1) which data in a local database fragment is part of the query result, and (2) how to gather the missing parts. We define partitioning and cache invariants that ensure that even partial matches on cached data are exploited and that correct answers are returned, despite our dynamic query-driven caching. Experimental results demonstrate that our techniques dramatically increase query throughputs and decrease query response times in wide area sensor databases.

We describe an efficient peer-to-peer information retrieval system, pSearch, that supports state-of-the-art content- and semantic-based full-text searches. pSearch avoids the scalability problem of existing systems that employ centralized indexing, or index/query flooding. It also avoids the nondeterminism that is exhibited by heuristic-based approaches. In pSearch, documents in the network are organized around their vector representations (based on modern document ranking algorithms) such that the search space for a given query is organized around related documents, achieving both eciency and accuracy.

The success of a P2P file-sharing network highly depends on the scalability and versatility of its search mechanism. Two particularly desirable search features are scope (ability to find infrequent items) and support for partial-match queries (queries that contain typos or include a subset of keywords). While centralized-index architectures (such as Napster) can support both these features, existing decentralized architectures seem to support at most one: prevailing unstructured P2P protocols (such as Gnutella and FastTrack) deploy a "blind" search mechanism where the set of peers probed is unrelated to the query; thus they support partial-match queries but have limited scope. On the other extreme, the recently-proposed distributed hash tables (DHTs) such as CAN and CHORD, couple index location with the item's hash value, and thus have good scope but can not effectively support partial-match queries. Another hurdle to DHTs deployment is their tight control of the overlay structure and the information (part of the index) each peer maintains, which makes them more sensitive to failures and frequent joins and disconnects.

In peer-to-peer networks, finding the appropriate answer for an information request, such as the answer to a query for RDF(S) data, depends on selecting the right peer in the network. We here investigate how social metaphors can be exploited effectively and efficiently to solve this task. To this end, we define a method for query routing, REMINDIN', that lets (i) peers observe which queries are successfully answered by other peers, (ii), memorizes this observation, and, (iii), subsequently uses this information in order to select peers to forward requests to.

Peer-to-Peer networks are gaining increasing attention from both the scientific and the large Internet user community. Popular applications utilizing this new technology offer many attractive features to a growing number of users. At the heart of such networks lies the search algorithm. Proposed methods either depend on the network-disastrous flooding and its variations or utilize various indices too expensive to maintain. In this paper, we describe an adaptive, bandwidth-efficient algorithm for search in unstructured Peer-to-Peer networks, the Adaptive Probabilistic Search method (APS). Our scheme utilizes feedback from previous searches to probabilistically guide future ones. It performs efficient object discovery while inducing zero overhead over dynamic network operations. Extensive simulation results show that APS achieves high success rates, increased number of discovered objects, very low bandwidth consumption and adaptation to changing topologies. 1.

The ability to efficiently discover information using partial knowledge (for example keywords, attributes or ranges) is important in large, decentralized, resource sharing distributed environments such as computational Grids and Peer-to-Peer (P2P) storage and retrieval systems. This paper presents a P2P information discovery system that supports flexible queries using partial keywords and wildcards, and range queries. It guarantees that all existing data elements that match a query are found with bounded costs in terms of number of messages and number of peers involved. The key innovation is a dimension reducing indexing scheme that effectively maps the multidimensional information space to physical peers. The design, implementation and experimental evaluation of the system are presented.

In this paper, we address the problem of designing a scalable, accurate query processor for peerto -peer filesharing and similar distributed keyword search systems. Using a globally-distributed monitoring infrastructure, we perform an extensive study of the Gnutella filesharing network, characterizing its topology, data and query workloads. We observe

Peer-to-peer systems are gaining popularity as a means to effectively share huge, massively distributed data collections. An important challenge in this context is discovering the appropriate data and services. In this paper, we consider peers that store XML documents. We show how an extension of traditional Bloom filters, called multi-level Bloom filters, can be used to route path queries in such a system. Two alternative ways are considered for building overlay networks of peers: one based on network proximity and one based on content similarity. Content similarity is derived from the similarity among filters. Our experimental results show that networks based on content similarity outperform those formed based on network proximity for finding all matching documents. 1.

The Keyword-Set Search System (KSS) is a Peer-to-Peer (P2P) keyword search system that uses a distributed inverted index. The main challenge in a distributed index and search system is finding the right scheme to partition the index across the nodes in the network. The most obvious scheme would be to partition the index by keyword.