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Open AccessProceedings ArticleDOI

Efficient peer-to-peer keyword searching

TLDR
A distributed search engine based on a distributed hash table is designed and analyzed and the simulation results predict that the search engine can answer an average query in under one second, using under one kilobyte of bandwidth.
Abstract
The recent file storage applications built on top of peer-to-peer distributed hash tables lack search capabilities. We believe that search is an important part of any document publication system. To that end, we have designed and analyzed a distributed search engine based on a distributed hash table. Our simulation results predict that our search engine can answer an average query in under one second, using under one kilobyte of bandwidth.

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Dissertation

Efficient data search and synchronization for tree-structured data on mobile devices

Mengyu Wang
TL;DR: This paper aims to provide a history of the field and some of the techniques used in its development, as well as some of its subsequent studies, to help clarify its origins.
Dissertation

Approximate algorithms for efficient indexing, clustering, and classification in Peer-to-peer networks.

TL;DR: A novel collaborative classification method constructed over a P2P network, which enables the participating users to enhance their classifiers by exchanging reduced, highly compact classification models, and scales to large networks.

Distributed Search with Rendezvous Search Systems

TL;DR: This talk wants to give a short overview of the current state of the art of rendezvous search systems by separating the matching of queries and data from the network communication.
Proceedings ArticleDOI

A Peer-to-Peer Based Text Sharing and Retrieval System

TL;DR: A text sharing and retrieval system which is layered on top of an unstructured P2P overlay network where every autonomous peer has powerful indexing and searching ability and has significant scalability.
Proceedings ArticleDOI

Bloom Filter Tree for Fast Search and Synchronization of Tree-Structured Data

TL;DR: This work proposes to compute bloom filters for the interior tree nodes, this bloom filter tree is used for both data search and synchronization, and presents a theroetical analysis of the search complexity of selective placement of bloom filters in the tree leading to an optimal placement strategy.
References
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Journal ArticleDOI

The anatomy of a large-scale hypertextual Web search engine

TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Proceedings Article

The PageRank Citation Ranking : Bringing Order to the Web

TL;DR: This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.
Journal Article

The Anatomy of a Large-Scale Hypertextual Web Search Engine.

Sergey Brin, +1 more
- 01 Jan 1998 - 
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Proceedings ArticleDOI

Chord: A scalable peer-to-peer lookup service for internet applications

TL;DR: Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes.
Journal ArticleDOI

Space/time trade-offs in hash coding with allowable errors

TL;DR: Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.
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