Approximate Information Filtering in Peer-to-Peer Networks
Identifieur interne : 000B41 ( Istex/Corpus ); précédent : 000B40; suivant : 000B42Approximate Information Filtering in Peer-to-Peer Networks
Auteurs : Christian Zimmer ; Christos Tryfonopoulos ; Klaus Berberich ; Manolis Koubarakis ; Gerhard WeikumSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
English descriptors
- Teeft :
- Approximate information, Baseline approach, Catchg, Centralized system, Continuous queries, Continuous query, Data sources, Database, Different publishing scenarios, Different topics, Digital libraries, Distinct terms, Document, Document collection, Document publications, Exact information, Experimental evaluation, Future publishing activity, Important observation, Inactivity periods, Information producer, Information retrieval, Load balancing, Local query database, Ltering, Ltering approaches, Maintenance messages, Maps protocols, Message cost, News items, Node, Node behavior, Node behavior prediction, Node score, Node selection, Node selection strategy, Nodes publishing documents, Olympic games, Other hand, Overall number, Overlay networks, Potential sources, Prediction mechanism, Prediction methods, Protocol, Publication prediction, Publication time, Publisher node, Publishing documents, Query, Query terms, Rand, Ranking function, Ranking method, Ranking methods, Relevant documents, Resource selection, Retrieval, Scenario, Scenario rand benefit cost ratio, Subscriber, Subscriber node, Subscription messages, Subscription protocol, Tats message, Time series, Time series analysis, Tmpchg, Tmpchg scenario, Topic authorities, Total number, User query, Zimmer.
Abstract
Abstract: Most approaches to information filtering taken so far have the underlying hypothesis of potentially delivering notifications from every information producer to subscribers. This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate information filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate information filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.
Url:
DOI: 10.1007/978-3-540-85481-4_3
Links to Exploration step
ISTEX:6C94997F67D3C6E499EFB0CFF88A2416015BC52ELe document en format XML
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Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.</abstract><qualityIndicators><score>8.24</score><pdfWordCount>5825</pdfWordCount><pdfCharCount>34941</pdfCharCount><pdfVersion>1.6</pdfVersion><pdfPageCount>14</pdfPageCount><pdfPageSize>430 x 660 pts</pdfPageSize><refBibsNative>false</refBibsNative><abstractWordCount>145</abstractWordCount><abstractCharCount>1046</abstractCharCount><keywordCount>0</keywordCount></qualityIndicators><title>Approximate Information Filtering in Peer-to-Peer Networks</title><chapterId><json:string>3</json:string><json:string>Chap3</json:string></chapterId><genre><json:string>conference</json:string></genre><serie><title>Lecture Notes in Computer Science</title><language><json:string>unknown</json:string></language><copyrightDate>2008</copyrightDate><issn><json:string>0302-9743</json:string></issn><eissn><json:string>1611-3349</json:string></eissn><editor><json:item><name>David Hutchison</name></json:item><json:item><name>Takeo Kanade</name></json:item><json:item><name>Josef Kittler</name></json:item><json:item><name>Jon M. 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This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate information filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate information filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.</p></abstract><textClass><keywords scheme="Book-Subject-Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book-Subject-Group"><list><label>I</label><item><term>Computer Science</term></item><label>I18040</label><item><term>Information Systems Applications (incl.Internet)</term></item><label>I18030</label><item><term>Data Mining and Knowledge Discovery</term></item><label>I18024</label><item><term>Database Management</term></item><label>I21017</label><item><term>Artificial Intelligence (incl. Robotics)</term></item><label>I24040</label><item><term>Computers and Society</term></item><label>W26007</label><item><term>Business Information Systems</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2008">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/6C94997F67D3C6E499EFB0CFF88A2416015BC52E/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName><PublisherLocation>Berlin, Heidelberg</PublisherLocation></PublisherInfo><Series><SeriesInfo SeriesType="Series" TocLevels="0"><SeriesID>558</SeriesID><SeriesPrintISSN>0302-9743</SeriesPrintISSN><SeriesElectronicISSN>1611-3349</SeriesElectronicISSN><SeriesTitle Language="En">Lecture Notes in Computer Science</SeriesTitle></SeriesInfo><SeriesHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Hutchison</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Takeo</GivenName><FamilyName>Kanade</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Josef</GivenName><FamilyName>Kittler</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Jon</GivenName><GivenName>M.</GivenName><FamilyName>Kleinberg</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Friedemann</GivenName><FamilyName>Mattern</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>John</GivenName><GivenName>C.</GivenName><FamilyName>Mitchell</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Moni</GivenName><FamilyName>Naor</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Oscar</GivenName><FamilyName>Nierstrasz</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>C.</GivenName><FamilyName>Pandu Rangan</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Bernhard</GivenName><FamilyName>Steffen</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Madhu</GivenName><FamilyName>Sudan</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Demetri</GivenName><FamilyName>Terzopoulos</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Doug</GivenName><FamilyName>Tygar</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Moshe</GivenName><GivenName>Y.</GivenName><FamilyName>Vardi</FamilyName></EditorName></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Weikum</FamilyName></EditorName></Editor></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingStyle="Unnumbered" OutputMedium="All" TocLevels="0"><BookID>978-3-540-85481-4</BookID><BookTitle>Web Information Systems Engineering - WISE 2008</BookTitle><BookSubTitle>9th International Conference, Auckland, New Zealand, September 1-3, 2008. Proceedings</BookSubTitle><BookVolumeNumber>5175</BookVolumeNumber><BookSequenceNumber>5175</BookSequenceNumber><BookDOI>10.1007/978-3-540-85481-4</BookDOI><BookTitleID>177655</BookTitleID><BookPrintISBN>978-3-540-85480-7</BookPrintISBN><BookElectronicISBN>978-3-540-85481-4</BookElectronicISBN><BookChapterCount>33</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I18040" Priority="1" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I18030" Priority="2" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="I18024" Priority="3" Type="Secondary">Database Management</BookSubject><BookSubject Code="I21017" Priority="4" Type="Secondary">Artificial Intelligence (incl. 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This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate information filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate information filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Approximate Information Filtering in Peer-to-Peer Networks</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Approximate Information Filtering in Peer-to-Peer Networks</title></titleInfo><name type="personal"><namePart type="given">Christian</namePart><namePart type="family">Zimmer</namePart><affiliation>Max-Planck-Institute for Informatics, Saarbrücken, Germany</affiliation><affiliation>E-mail: czimmer@mpi-inf.mpg.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Christos</namePart><namePart type="family">Tryfonopoulos</namePart><affiliation>Max-Planck-Institute for Informatics, Saarbrücken, Germany</affiliation><affiliation>E-mail: trifon@mpi-inf.mpg.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Klaus</namePart><namePart type="family">Berberich</namePart><affiliation>Max-Planck-Institute for Informatics, Saarbrücken, Germany</affiliation><affiliation>E-mail: kberberi@mpi-inf.mpg.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Manolis</namePart><namePart type="family">Koubarakis</namePart><affiliation>National and Kapodistrian University of Athens, Greece</affiliation><affiliation>E-mail: koubarak@di.uoa.gr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck-Institute for Informatics, Saarbrücken, Germany</affiliation><affiliation>E-mail: weikum@mpi-inf.mpg.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Most approaches to information filtering taken so far have the underlying hypothesis of potentially delivering notifications from every information producer to subscribers. This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate information filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate information filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.</abstract><relatedItem type="host"><titleInfo><title>Web Information Systems Engineering - WISE 2008</title><subTitle>9th International Conference, Auckland, New Zealand, September 1-3, 2008. 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