Trust-aware information retrieval in peer-to-peer environments

Information Retrieval in P2P environments (P2PIR) has become an active field of research due to the observation that P2P architectures have the potential to become as appealing as traditional centralised architectures. P2P networks are formed with voluntary peers that exchange information and accomplish various tasks. Some of them may be malicious peers spreading untrustworthy resources. However, existing P2PIR systems only focus on finding relevant documents, while trustworthiness of documents and document providers has been ignored. Without prior experience and knowledge about the network, users run the risk to review,download and use untrustworthy documents, even if these documents are relevant. The work presented in this dissertation provide the first integrated framework for trust-aware Information Retrieval in P2P environments, which can retrieve not only relevant but also trustworthy documents. The proposed content trust models extend an existing P2P trust management system, PeerTrust, in the context of P2PIR to compute the trust values of documents and document providers for given queries. A method is proposed to estimate global term statistics which are integrated with existing relevance-based approaches for document ranking and peer selection. Different approaches are explored to find optimal parametersettings in the proposed trust-aware P2PIR systems. Moreover, system architectures and data management protocols are designed to implement the proposed trust-aware P2PIR systems in structured P2P networks. The experimental evaluation demonstrates that P2PIR can benefit from trust-aware P2PIR systems significantly. It can importantly reduce the possibility of untrustworthy documents in the top-ranked result list. The proposed estimated global term statistics can provide acceptable and competitive retrieval accuracy within different P2PIR scenarios.EThOS - Electronic Theses Online ServiceORSSchool ScholarshipGBUnited Kingdo

P2P Information Retrieval and Filtering with MAPS

Abstract In this demonstration paper we present MAPS, a novel system that combines approximate information retrieval and filtering functionality in a peer-to-peer setting. In MAPS, a user is able to submit one-time and continuous queries, and receive matching resources and notifications from selected information sources. The selection of these sources in the retrieval case is based on well-known resource selection techniques for peer-to-peer query routing, while in the filtering case a combination of resource selection and novel behavior prediction techniques using timeseries analysis of publisher statistics is used. The integration of the two functionalities is done in a seamless way utilizing the same machinery: a conceptually global, but physically distributed directory of statistics about information sources based on distributed hash tables

P2P Information Retrieval and Filtering with MAPS (Demo)

In this demonstration paper we present MAPS, a novel system that combines approximate information retrieval and filtering functionality in a peer-to-peer setting. In MAPS, a user is able to submit one-time and continuous queries, and receive matching resources and notifications from selected information sources. The selection of these sources in the retrieval case is based on well-known resource selection techniques for peer-to-peer query routing, while in the filtering case a combination of resource selection and novel behavior prediction techniques using time-series analysis of publisher statistics is used. The integration of the two functionalities is done in a seamless way utilizing the same machinery: a conceptually global, but physically distributed directory of statistics about information sources based on distributed hash tables