Improved algorithms for topic distillation in a hyperlinked environment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Web Searching and Information Retrieval
Computing in Science and Engineering
Progressive Distributed Top-k Retrieval in Peer-to-Peer Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Semantic link based top-K join queries in P2P networks
Proceedings of the 15th international conference on World Wide Web
Reducing network traffic in unstructured P2P systems using Top-k queries
Distributed and Parallel Databases
Efficient and decentralized PageRank approximation in a peer-to-peer web search network
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Swarm intelligent surfing in the web
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Top-k query processing in the APPA P2P system
VECPAR'06 Proceedings of the 7th international conference on High performance computing for computational science
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Search engines are among the most important applications or services on the web. Most existing successful search engines use global ranking algorithms to generate the ranking of documents crawled in their databases. However, global ranking of documents has two potential problems: high computation cost and potentially poor rankings. Both of the problems are related to the centralized computation paradigm. We propose to decentralize the task of ranking. This requires two things: a decentralized architecture and a logical framework for ranking computation. In the paper we introduce a ranking algebra providing such a formal framework. Through partitioning and combining rankings, we manage to compute document rankings of large-scale web data sets in a localized fashion. We provide initial results, demonstrating that the use of such an approach can ameliorate the above-mentioned problems. The approach presents a step towards P2P Web search engines.