Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

Scalable Peer-to-Peer Indexing with Constant State

We present a distributed indexing scheme for peer to peer networks. Past work on distributed indexing traded off fast search times with non-constant degree topologies or network-unfriendly behavior such as flooding. In contrast, the scheme we present optimizes all three of these performance measures. That is, we provide logarithmic round searches while maintaining connections to a fixed number of peers and avoiding network flooding. In comparison to the well known scheme Chord, we provide competitive constant factors. Finally, we observe that arbitrary linear speedups are possible and discuss both a general brute force approach and specific economical optimizations

Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

2 P2P or Not 2 P2P?

In the hope of stimulating discussion, we present a heuristic decision tree that designers can use to judge the likely suitability of a P2P architecture for their applications. It is based on the characteristics of a wide range of P2P systems from the literature, both proposed and deployed.Comment: 6 pages, 1 figur

Porqpine: a peer-to-peer search engine

In this paper, we present a fully distributed and collaborative search engine for web pages: Porqpine. This system uses a novel query-based model and collaborative filtering techniques in order to obtain user-customized results. All knowledge about users and profiles is stored in each user node?s application. Overall the system is a multi-agent system that runs on the computers of the user community. The nodes interact in a peer-to-peer fashion in order to create a real distributed search engine where information is completely distributed among all the nodes in the network. Moreover, the system preserves the privacy of user queries and results by maintaining the anonymity of the queries? consumers and results? producers. The knowledge required by the system to work is implicitly caught through the monitoring of users actions, not only within the system?s interface but also within one of the most popular web browsers. Thus, users are not required to explicitly feed knowledge about their interests into the system since this process is done automatically. In this manner, users obtain the benefits of a personalized search engine just by installing the application on their computer. Porqpine does not intend to shun completely conventional centralized search engines but to complement them by issuing more accurate and personalized results.Postprint (published version