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SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Wide-area cooperative storage with CFS
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
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ACM SIGCOMM Computer Communication Review
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SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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IEEE/ACM Transactions on Networking (TON)
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Middleware '01 Proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms Heidelberg
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ICDCS '99 Proceedings of the 19th IEEE International Conference on Distributed Computing Systems
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Journal of Parallel and Distributed Computing
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
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World Wide Web
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Nowadays "live" content, such as weblog, wikipedia, and news, is ubiquitous in the Internet. Providing users with relevant content in a timely manner becomes a challenging problem. Differing from Web search technologies and RSS feeds/reader applications, this paper envisions a personalized full-text content filtering and dissemination system in a highly distributed environment such as a Distributed Hash Table (DHT) based Peer-to-Peer (P2P) Network. Users subscribe to their interested content by specifying input keywords and thresholds as filters. Then, content is disseminated to those users having interest in it. In the literature, full-text document publishing in DHTs has suffered for a long time from the high cost of forwarding a document to home nodes of all distinct terms. It is aggravated by the fact that a document contains a large number of distinct terms (typically tens or thousands of terms per document). In this paper, we propose a set of novel techniques to overcome such a high forwarding cost by carefully selecting a very small number of meaningful terms (or key features) among candidate terms inside each document. Next, to reduce the average hop count per forwarding, we further prune irrelevant documents during the forwarding path. Experiments based on two real query logs and two real data sets demonstrate the effectiveness of our solution.