PUMA publications for /user/stephandoerfel/mininghttps://puma.uni-kassel.de/user/stephandoerfel/miningPUMA RSS feed for /user/stephandoerfel/mining2024-03-29T12:15:22+01:00Face-to-Face Contacts during a Conference: Communities, Roles, and Key Playershttps://puma.uni-kassel.de/bibtex/21fe037ea2712b205c564243d67840059/stephandoerfelstephandoerfel2011-09-16T12:21:49+02:002011 analysis communities community discovery face itegpub knowledge mining myown rfid venus venuspub <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Atzmueller" itemprop="url" href="/author/Martin%20Atzmueller"><span itemprop="name">M. Atzmueller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011</span>, </em></span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Fri Sep 16 12:21:49 CEST 2011Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players20112011 analysis communities community discovery face itegpub knowledge mining myown rfid venus venuspub Characterizing and Mining the Citation Graph of the Computer Science Literaturehttps://puma.uni-kassel.de/bibtex/260e0c625f5765a05c588c6765a8cd93c/stephandoerfelstephandoerfel2011-11-07T09:23:27+01:0010th Citation characterizing citation computer graph mining <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yuan An" itemprop="url" href="/author/Yuan%20An"><span itemprop="name">Y. An</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jeannette Janssen" itemprop="url" href="/author/Jeannette%20Janssen"><span itemprop="name">J. Janssen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Evangelos E. Milios" itemprop="url" href="/author/Evangelos%20E.%20Milios"><span itemprop="name">E. Milios</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Knowl. Inf. Syst.</em></span></span> </span>(<em><span>November 2004<meta content="November 2004" itemprop="datePublished"/></span></em>)Mon Nov 07 09:23:27 CET 2011New York, NY, USAKnowl. Inf. Syst.November664--678Characterizing and Mining the Citation Graph of the Computer Science Literature6200410th Citation characterizing citation computer graph mining Twitter as a Corpus for Sentiment Analysis and Opinion Mininghttps://puma.uni-kassel.de/bibtex/2ba1358f07702423b60c9e94f8aa5985c/stephandoerfelstephandoerfel2012-02-29T10:36:49+01:00emotion mining seminar sentiment twitter <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Pak" itemprop="url" href="/author/Alexander%20Pak"><span itemprop="name">A. Pak</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Patrick Paroubek" itemprop="url" href="/author/Patrick%20Paroubek"><span itemprop="name">P. Paroubek</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span><em> 2010, </em><em>Seite <span itemprop="pagination">1320-1326</span>. </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Wed Feb 29 10:36:49 CET 20121320-1326Twitter as a Corpus for Sentiment Analysis and Opinion Mining20102010emotion mining seminar sentiment twitter Twitter as a Corpus for Sentiment Analysis and Opinion Mining | MendeleyFace-to-Face Contacts at a Conference: Dynamics of Communities and Roleshttps://puma.uni-kassel.de/bibtex/2d81d6f6ccdf3ff6572898d39c90e6354/stephandoerfelstephandoerfel2012-09-19T22:47:35+02:002012 conference contacts dynamics face intelligence itegpub kde mining myown social ubiquitous venus venuspub <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Atzmueller" itemprop="url" href="/author/Martin%20Atzmueller"><span itemprop="name">M. Atzmueller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Modeling and Mining Ubiquitous Social Media</span>, </em><em>Volume 7472 von LNAI, </em><em><span itemprop="publisher">Springer Verlag</span>, </em><em>Heidelberg, Germany, </em></span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Wed Sep 19 22:47:35 CEST 2012Heidelberg, Germany{Modeling and Mining Ubiquitous Social Media}LNAIFace-to-Face Contacts at a Conference: Dynamics of Communities and Roles747220122012 conference contacts dynamics face intelligence itegpub kde mining myown social ubiquitous venus venuspub Usage analysis of a digital libraryhttps://puma.uni-kassel.de/bibtex/2b61a4bcdb642dd45d2144a7181121f1f/stephandoerfelstephandoerfel2013-03-28T14:40:55+01:00analysis digital library logs mining usage weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Jones" itemprop="url" href="/author/Steve%20Jones"><span itemprop="name">S. Jones</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sally Jo Cunningham" itemprop="url" href="/author/Sally%20Jo%20Cunningham"><span itemprop="name">S. Cunningham</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rodger McNab" itemprop="url" href="/author/Rodger%20McNab"><span itemprop="name">R. McNab</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the third ACM conference on Digital libraries</span>, </em></span><em>Seite <span itemprop="pagination">293--294</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>1998<meta content="1998" itemprop="datePublished"/></span></em>)Thu Mar 28 14:40:55 CET 2013New York, NY, USAProceedings of the third ACM conference on Digital libraries293--294DL '98Usage analysis of a digital library1998analysis digital library logs mining usage weblog Usage analysis of a digital libraryScholarly journal usage: the results of deep log analysishttps://puma.uni-kassel.de/bibtex/28e733e3b55a1a648c6e5070d347c43dc/stephandoerfelstephandoerfel2013-03-28T15:13:09+01:00Scholarly analysis deep journal log mining usage weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David Nicholas" itemprop="url" href="/author/David%20Nicholas"><span itemprop="name">D. Nicholas</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paul Huntington" itemprop="url" href="/author/Paul%20Huntington"><span itemprop="name">P. Huntington</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anthony Watkinson" itemprop="url" href="/author/Anthony%20Watkinson"><span itemprop="name">A. Watkinson</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of Documentation</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">61 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">248--280</span></em> </span>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Thu Mar 28 15:13:09 CET 2013BingleyJournal of Documentation2248--280Scholarly journal usage: the results of deep log analysis612005Scholarly analysis deep journal log mining usage weblog Emerald | Journal of Documentation | Scholarly journal usage: the results of deep log analysisAnalysis of a very large web search engine query loghttps://puma.uni-kassel.de/bibtex/24ac734beeccbcb3a05786e8ca57f5629/stephandoerfelstephandoerfel2013-03-29T19:20:07+01:00altavista behaviour engine log mining query search weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Craig Silverstein" itemprop="url" href="/author/Craig%20Silverstein"><span itemprop="name">C. Silverstein</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hannes Marais" itemprop="url" href="/author/Hannes%20Marais"><span itemprop="name">H. Marais</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Monika Henzinger" itemprop="url" href="/author/Monika%20Henzinger"><span itemprop="name">M. Henzinger</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael Moricz" itemprop="url" href="/author/Michael%20Moricz"><span itemprop="name">M. Moricz</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>SIGIR Forum</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">33 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">6--12</span></em> </span>(<em><span>September 1999<meta content="September 1999" itemprop="datePublished"/></span></em>)Fri Mar 29 19:20:07 CET 2013New York, NY, USASIGIR Forumsep16--12Analysis of a very large web search engine query log331999altavista behaviour engine log mining query search weblog In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.Analysis of a very large web search engine query logSearch log analysis: What it is, what's been done, how to do ithttps://puma.uni-kassel.de/bibtex/2e147f866b624d461c77a24b79b2d9aff/stephandoerfelstephandoerfel2013-03-29T21:59:41+01:00analysis howto log mining search weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bernard J. Jansen" itemprop="url" href="/author/Bernard%20J.%20Jansen"><span itemprop="name">B. Jansen</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Library & Information Science Research</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">28 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">407 - 432</span></em> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Mar 29 21:59:41 CET 2013Library & Information Science Research3407 - 432Search log analysis: What it is, what's been done, how to do it282006analysis howto log mining search weblog The use of data stored in transaction logs of Web search engines, Intranets, and Web sites can provide valuable insight into understanding the information-searching process of online searchers. This understanding can enlighten information system design, interface development, and devising the information architecture for content collections. This article presents a review and foundation for conducting Web search transaction log analysis. A methodology is outlined consisting of three stages, which are collection, preparation, and analysis. The three stages of the methodology are presented in detail with discussions of goals, metrics, and processes at each stage. Critical terms in transaction log analysis for Web searching are defined. The strengths and limitations of transaction log analysis as a research method are presented. An application to log client-side interactions that supplements transaction logs is reported on, and the application is made available for use by the research community. Suggestions are provided on ways to leverage the strengths of, while addressing the limitations of, transaction log analysis for Web-searching research. Finally, a complete flat text transaction log from a commercial search engine is available as supplementary material with this manuscript.ScienceDirect.com - Library & Information Science Research - Search log analysis: What it is, what's been done, how to do itPreprocessing and Mining Web Log Data for Web Personalizationhttps://puma.uni-kassel.de/bibtex/21607f6c312b64832bab33fa843442d5e/stephandoerfelstephandoerfel2013-03-30T15:52:31+01:00classification gender interest mining user weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Baglioni" itemprop="url" href="/author/M.%20Baglioni"><span itemprop="name">M. Baglioni</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="U. Ferrara" itemprop="url" href="/author/U.%20Ferrara"><span itemprop="name">U. Ferrara</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Romei" itemprop="url" href="/author/A.%20Romei"><span itemprop="name">A. Romei</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Ruggieri" itemprop="url" href="/author/S.%20Ruggieri"><span itemprop="name">S. Ruggieri</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="F. Turini" itemprop="url" href="/author/F.%20Turini"><span itemprop="name">F. Turini</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">AI*IA 2003: Advances in Artificial Intelligence</span>, </em><em>Volume 2829 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer Berlin Heidelberg</span>, </em></span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Sat Mar 30 15:52:31 CET 2013AI*IA 2003: Advances in Artificial Intelligence237-249Lecture Notes in Computer SciencePreprocessing and Mining Web Log Data for Web Personalization28292003classification gender interest mining user weblog We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site.Preprocessing and Mining Web Log Data for Web Personalization - SpringerUnderstanding user goals in web searchhttps://puma.uni-kassel.de/bibtex/2527fa40ab61aa9987608eed21e3d43eb/stephandoerfelstephandoerfel2013-03-30T16:06:04+01:00goal intention mining search user weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel E. Rose" itemprop="url" href="/author/Daniel%20E.%20Rose"><span itemprop="name">D. Rose</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Danny Levinson" itemprop="url" href="/author/Danny%20Levinson"><span itemprop="name">D. Levinson</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 13th international conference on World Wide Web</span>, </em></span><em>Seite <span itemprop="pagination">13--19</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Sat Mar 30 16:06:04 CET 2013New York, NY, USAProceedings of the 13th international conference on World Wide Web13--19WWW '04Understanding user goals in web search2004goal intention mining search user weblog Previous work on understanding user web search behavior has focused on how people search and what they are searching for, but not why they are searching. In this paper, we describe a framework for understanding the underlying goals of user searches, and our experience in using the framework to manually classify queries from a web search engine. Our analysis suggests that so-called navigational" searches are less prevalent than generally believed while a previously unexplored "resource-seeking" goal may account for a large fraction of web searches. We also illustrate how this knowledge of user search goals might be used to improve future web search engines.Understanding user goals in web searchFrequent Pattern Mining in Web Log Datahttps://puma.uni-kassel.de/bibtex/2f29f4627c9ae99370fc7ba005982e2e6/stephandoerfelstephandoerfel2013-03-30T17:32:37+01:00association frequent itemset mining pattern rule weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Renáta Iváncsy" itemprop="url" href="/author/Ren%c3%a1ta%20Iv%c3%a1ncsy"><span itemprop="name">R. Iváncsy</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="István Vajk" itemprop="url" href="/author/Istv%c3%a1n%20Vajk"><span itemprop="name">I. Vajk</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Acta Polytechnica Hungarica</em></span></span> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Sat Mar 30 17:32:37 CET 2013Acta Polytechnica Hungarica1Frequent Pattern Mining in Web Log Data32006association frequent itemset mining pattern rule weblog Abstract: Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. One of them is to use frequent pattern discovery methods in Web log data. Discovering hidden information from Web log data is called Web usage mining. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. In this paper three pattern mining approaches are investigated from the Web usage mining point of view. The different patterns in Web log mining are page sets, page sequences and page graphs.CiteSeerX — Frequent Pattern Mining in Web Log DataIdentifying User Behavior by Analyzing Web Server
Access Log Filehttps://puma.uni-kassel.de/bibtex/2409a7c28e5ab6edebfaccb4d2f4c1503/stephandoerfelstephandoerfel2013-03-30T18:45:21+01:00asociation count hit mining rules weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K. R. Suneetha" itemprop="url" href="/author/K.%20R.%20Suneetha"><span itemprop="name">K. Suneetha</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K. R. Krishnamoorthy" itemprop="url" href="/author/K.%20R.%20Krishnamoorthy"><span itemprop="name">K. Krishnamoorthy</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>International Journal of Computer Science and Network Security</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">9 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">327-332</span></em> </span>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Sat Mar 30 18:45:21 CET 2013International Journal of Computer Science and Network Security4327-332Identifying User Behavior by Analyzing Web Server
Access Log File92005asociation count hit mining rules weblog Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional Comparisonshttps://puma.uni-kassel.de/bibtex/2f4c269e2086b8624c1c0c59ed075d677/stephandoerfelstephandoerfel2013-03-31T14:05:05+02:00Diversity behaviour mining oscholar seeking weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David Nicholas" itemprop="url" href="/author/David%20Nicholas"><span itemprop="name">D. Nicholas</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paul Huntington" itemprop="url" href="/author/Paul%20Huntington"><span itemprop="name">P. Huntington</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hamid R. Jamali" itemprop="url" href="/author/Hamid%20R.%20Jamali"><span itemprop="name">H. Jamali</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>The Journal of Academic Librarianship</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">33 </span></span>(<span itemprop="issueNumber">6</span>):
<span itemprop="pagination">629 - 638</span></em> </span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Sun Mar 31 14:05:05 CEST 2013The Journal of Academic Librarianship6629 - 638Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional Comparisons332007Diversity behaviour mining oscholar seeking weblog The logs of four universities using the OhioLINK journal system were evaluated for a period of fifteen months using deep log analysis methods in order to compare and contrast the information seeking behaviour of their users. Large differences were found, especially between the research and teaching universities. Methodological problems associated with making the comparisons are discussed in some detail especially in terms of defining online sessions.ScienceDirect.com - The Journal of Academic Librarianship - Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional ComparisonsWebsite usage metrics: A re-assessment of session datahttps://puma.uni-kassel.de/bibtex/2565f363f36a9a0a14c7ac44824ec91ad/stephandoerfelstephandoerfel2013-03-31T14:47:23+02:00mining proxy session timeout weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paul Huntington" itemprop="url" href="/author/Paul%20Huntington"><span itemprop="name">P. Huntington</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David Nicholas" itemprop="url" href="/author/David%20Nicholas"><span itemprop="name">D. Nicholas</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hamid R. Jamali" itemprop="url" href="/author/Hamid%20R.%20Jamali"><span itemprop="name">H. Jamali</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Information Processing & Management</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">44 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">358 - 372</span></em> </span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)<em>Evaluation of Interactive Information Retrieval Systems.</em>Sun Mar 31 14:47:23 CEST 2013Information Processing & ManagementEvaluation of Interactive Information Retrieval Systems1358 - 372Website usage metrics: A re-assessment of session data442008mining proxy session timeout weblog Metrics derived from user visits or sessions provide a means of evaluating Websites and an important insight into online information seeking behaviour, the most important of them being the duration of sessions and the number of pages viewed in a session, a possible busyness indicator. However, the identification of session (termed often ‘sessionization’) is fraught with difficulty in that there is no way of determining from a transactional log file that a user has ended their session. No one logs out. Instead a session delimiter has to be applied and this is typically done on the basis of a standard period of inactivity. To date researchers have discussed the issue of a time out delimiter in terms of a single value and if a page view time exceeds the cut-off value the session is deemed to have ended. This approach assumes that page view time is a single distribution and that the cut-off value is one point on that distribution. The authors however argue that page time distribution is composed of a number of quite separate view time distributions because of the marked differences in view times between pages (abstract, contents page, full text). This implies that a number of timeout delimiters should be applied. Employing data from a study of the OhioLINK digital journal library, the authors demonstrate how the setting of a time out delimiter impacts on the estimate of page view time and the number of estimated session. Furthermore, they also show how a number of timeout delimiters might apply and they argue that this gives a better and more robust estimate of the number of sessions, session time and page view time compared to an application of a single timeout delimiter.ScienceDirect.com - Information Processing & Management - Website usage metrics: A re-assessment of session dataEnd user searching: A Web log analysis of NAVER, a Korean Web search enginehttps://puma.uni-kassel.de/bibtex/2fef68b6d2a607ef462592dd73295328d/stephandoerfelstephandoerfel2013-03-31T23:00:58+02:00mining query search term weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Soyeon Park" itemprop="url" href="/author/Soyeon%20Park"><span itemprop="name">S. Park</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joon Ho Lee" itemprop="url" href="/author/Joon%20Ho%20Lee"><span itemprop="name">J. Lee</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hee Jin Bae" itemprop="url" href="/author/Hee%20Jin%20Bae"><span itemprop="name">H. Bae</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Library & Information Science Research</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">27 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">203 - 221</span></em> </span>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Sun Mar 31 23:00:58 CEST 2013Library & Information Science Research2203 - 221End user searching: A Web log analysis of NAVER, a Korean Web search engine272005mining query search term weblog Transaction logs of NAVER, a major Korean Web search engine, were analyzed to track the information-seeking behavior of Korean Web users. These transaction logs include more than 40 million queries collected over 1 week. This study examines current transaction log analysis methodologies and proposes a method for log cleaning, session definition, and query classification. A term definition method which is necessary for Korean transaction log analysis is also discussed. The results of this study show that users behave in a simple way: they type in short queries with a few query terms, seldom use advanced features, and view few results' pages. Users also behave in a passive way: they seldom change search environments set by the system. It is of interest that users tend to change their queries totally rather than adding or deleting terms to modify the previous queries. The results of this study might contribute to the development of more efficient and effective Web search engines and services.ScienceDirect.com - Library & Information Science Research - End user searching: A Web log analysis of NAVER, a Korean Web search engineWeb log analysis: a review of a decade of studies about information acquisition, inspection and interpretation of user interactionhttps://puma.uni-kassel.de/bibtex/29b85b7d3c5587c5f0920f0d602ba93b1/stephandoerfelstephandoerfel2013-04-01T18:30:25+02:00mining review weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maristella Agosti" itemprop="url" href="/author/Maristella%20Agosti"><span itemprop="name">M. Agosti</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Franco Crivellari" itemprop="url" href="/author/Franco%20Crivellari"><span itemprop="name">F. Crivellari</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="GiorgioMaria Di Nunzio" itemprop="url" href="/author/GiorgioMaria%20Di%20Nunzio"><span itemprop="name">G. Di Nunzio</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Data Mining and Knowledge Discovery</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">24 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">663-696</span></em> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Mon Apr 01 18:30:25 CEST 2013Data Mining and Knowledge Discovery3663-696Web log analysis: a review of a decade of studies about information acquisition, inspection and interpretation of user interaction242012mining review weblog In the last decade, the importance of analyzing information management systems logs has grown, because log data constitute a relevant aspect in evaluating the quality of such systems. A review of 10 years of research on log analysis is presented in this paper. About 50 papers and posters from five major conferences and about 30 related journal papers have been selected to trace the history of the state-of-the-art in this field. The paper presents an overview of two main themes: Web search engine log analysis and Digital Library System log analysis. The problem of the analysis of different sources of log data and the distribution of data are investigated.Web log analysis: a review of a decade of studies about information acquisition, inspection and interpretation of user interaction - SpringerA statistical comparison of tag and query logshttps://puma.uni-kassel.de/bibtex/2d3e4319a20670f7f73bdf83b63bdf4c7/stephandoerfelstephandoerfel2013-04-01T22:35:32+02:00comparison diagram divergence kullblack-Leibler mining tags triangular vocabulary weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mark J. Carman" itemprop="url" href="/author/Mark%20J.%20Carman"><span itemprop="name">M. Carman</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mark Baillie" itemprop="url" href="/author/Mark%20Baillie"><span itemprop="name">M. Baillie</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Gwadera" itemprop="url" href="/author/Robert%20Gwadera"><span itemprop="name">R. Gwadera</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Fabio Crestani" itemprop="url" href="/author/Fabio%20Crestani"><span itemprop="name">F. Crestani</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval</span>, </em></span><em>Seite <span itemprop="pagination">123--130</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Mon Apr 01 22:35:32 CEST 2013New York, NY, USAProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval123--130SIGIR '09A statistical comparison of tag and query logs2009comparison diagram divergence kullblack-Leibler mining tags triangular vocabulary weblog We investigate tag and query logs to see if the terms people use to annotate websites are similar to the ones they use to query for them. Over a set of URLs, we compare the distribution of tags used to annotate each URL with the distribution of query terms for clicks on the same URL. Understanding the relationship between the distributions is important to determine how useful tag data may be for improving search results and conversely, query data for improving tag prediction. In our study, we compare both term frequency distributions using vocabulary overlap and relative entropy. We also test statistically whether the term counts come from the same underlying distribution. Our results indicate that the vocabulary used for tagging and searching for content are similar but not identical. We further investigate the content of the websites to see which of the two distributions (tag or query) is most similar to the content of the annotated/searched URL. Finally, we analyze the similarity for different categories of URLs in our sample to see if the similarity between distributions is dependent on the topic of the website or the popularity of the URL.A statistical comparison of tag and query logsUnderstanding why users tag: A survey of tagging motivation literature and results from an empirical studyhttps://puma.uni-kassel.de/bibtex/25c063dc162f38895336d2775507132ee/stephandoerfelstephandoerfel2013-04-05T16:16:16+02:00mining motivation survey tagging understanding users weblog <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus Strohmaier" itemprop="url" href="/author/Markus%20Strohmaier"><span itemprop="name">M. Strohmaier</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Körner" itemprop="url" href="/author/Christian%20K%c3%b6rner"><span itemprop="name">C. Körner</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Roman Kern" itemprop="url" href="/author/Roman%20Kern"><span itemprop="name">R. Kern</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Web Semantics: Science, Services and Agents on the World Wide Web</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">17 </span></span>(<span itemprop="issueNumber">0</span>):
<span itemprop="pagination">1 - 11</span></em> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Fri Apr 05 16:16:16 CEST 2013Web Semantics: Science, Services and Agents on the World Wide Web01 - 11Understanding why users tag: A survey of tagging motivation literature and results from an empirical study172012mining motivation survey tagging understanding users weblog While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.ScienceDirect.com - Web Semantics: Science, Services and Agents on the World Wide Web - Understanding why users tag: A survey of tagging motivation literature and results from an empirical studyUnderstanding Latent Interactions in Online Social Networkshttps://puma.uni-kassel.de/bibtex/2aa9695f56135fd58de32b5b4a4c73698/stephandoerfelstephandoerfel2014-07-15T10:39:30+02:00analyis interaction latent log mining network social user web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jing Jiang" itemprop="url" href="/author/Jing%20Jiang"><span itemprop="name">J. Jiang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christo Wilson" itemprop="url" href="/author/Christo%20Wilson"><span itemprop="name">C. Wilson</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiao Wang" itemprop="url" href="/author/Xiao%20Wang"><span itemprop="name">X. Wang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wenpeng Sha" itemprop="url" href="/author/Wenpeng%20Sha"><span itemprop="name">W. Sha</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peng Huang" itemprop="url" href="/author/Peng%20Huang"><span itemprop="name">P. Huang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yafei Dai" itemprop="url" href="/author/Yafei%20Dai"><span itemprop="name">Y. Dai</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ben Y. Zhao" itemprop="url" href="/author/Ben%20Y.%20Zhao"><span itemprop="name">B. Zhao</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>ACM Trans. Web</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">7 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">18:1--18:39</span></em> </span>(<em><span>November 2013<meta content="November 2013" itemprop="datePublished"/></span></em>)Tue Jul 15 10:39:30 CEST 2014New York, NY, USAACM Trans. Webnov418:1--18:39Understanding Latent Interactions in Online Social Networks72013analyis interaction latent log mining network social user web Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.Understanding latent interactions in online social networksMining Videos from the Web for Electronic Textbookshttps://puma.uni-kassel.de/bibtex/276ac675e26647d14199da79c3467bc90/stephandoerfelstephandoerfel2014-11-27T14:24:58+01:00application fca mining video <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rakesh Agrawal" itemprop="url" href="/author/Rakesh%20Agrawal"><span itemprop="name">R. Agrawal</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maria Christoforaki" itemprop="url" href="/author/Maria%20Christoforaki"><span itemprop="name">M. Christoforaki</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sreenivas Gollapudi" itemprop="url" href="/author/Sreenivas%20Gollapudi"><span itemprop="name">S. Gollapudi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anitha Kannan" itemprop="url" href="/author/Anitha%20Kannan"><span itemprop="name">A. Kannan</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Krishnaram Kenthapadi" itemprop="url" href="/author/Krishnaram%20Kenthapadi"><span itemprop="name">K. Kenthapadi</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Adith Swaminathan" itemprop="url" href="/author/Adith%20Swaminathan"><span itemprop="name">A. Swaminathan</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Formal Concept Analysis</span>, </em><em>Volume 8478 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer International Publishing</span>, </em></span>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Thu Nov 27 14:24:58 CET 2014Formal Concept Analysis219-234Lecture Notes in Computer ScienceMining Videos from the Web for Electronic Textbooks84782014application fca mining video We propose a system for mining videos from the web for supplementing the content of electronic textbooks in order to enhance their utility. Textbooks are generally organized into sections such that each section explains very few concepts and every concept is primarily explained in one section. Building upon these principles from the education literature and drawing upon the theory of Mining Videos from the Web for Electronic Textbooks - Springer