http://www.bibsonomy.org/burst/group/l3sBibSonomy BuRST Feed for /group/l3s2008-07-21T00:32:52+02:00SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2522c68b8bb5e28f1bf9f1e11e612f542/jaeschkejaeschke2008-07-14T15:41:59+02:00fca set algorithm frequent mining parallel itemset <span style="color:#555555;">Salvatore <a href="http://www.bibsonomy.org/author/Orlando">Orlando</a> and Paolo <a href="http://www.bibsonomy.org/author/Palmerini">Palmerini</a> and Raffaele <a href="http://www.bibsonomy.org/author/Perego">Perego</a> and Fabrizio <a href="http://www.bibsonomy.org/author/Silvestri">Silvestri</a> </span><em>High Performance Computing for Computational Science &#8212; VECPAR 2002, </em><em>page3--29. </em>(<em>2003</em>)Mon Jul 14 15:41:59 CEST 2008High Performance Computing for Computational Science — VECPAR 20023--29An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets2003fca set algorithm frequent mining parallel itemset Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.http://www.bibsonomy.org/bibtex/23aff1098bf9828a0c6683f07145d60bb/jaeschkejaeschke2008-07-14T13:37:17+02:00frequent itemset association closed rule mining fca <span style="color:#555555;">Claudio <a href="http://www.bibsonomy.org/author/Lucchese">Lucchese</a> and Salvatore <a href="http://www.bibsonomy.org/author/Orlando">Orlando</a> and Raffaele <a href="http://www.bibsonomy.org/author/Perego">Perego</a> </span><em>IEEE Transactions On Knowledge and Data Engineering</em><em>18(1):21--36</em>(<em>2006</em>)Mon Jul 14 13:37:17 CEST 2008IEEE Transactions On Knowledge and Data Engineering121--36Fast and Memory Efficient Mining of Frequent Closed Itemsets182006frequent itemset association closed rule mining fca http://www.bibsonomy.org/bibtex/2645abd6b3191a2a6e844d7542651ed1c/jaeschkejaeschke2008-07-12T13:03:47+02:00detection community clustering <span style="color:#555555;">Akshay <a href="http://www.bibsonomy.org/author/Java">Java</a> and Anupam <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> and Tim <a href="http://www.bibsonomy.org/author/Finin">Finin</a> </span><em>WebKDD 2008 Workshop on Web Mining and Web Usage Analysis, </em><em>August2008. </em><em>To Appear . </em>Sat Jul 12 13:03:47 CEST 2008WebKDD 2008 Workshop on Web Mining and Web Usage AnalysisAugustTo AppearDetecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies2008detection community clustering http://www.bibsonomy.org/bibtex/2b54f6557893e3ab9d1eb83b0baeb136e/stummestumme2008-07-04T15:05:56+02:00bookmarking folksonomy social <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>page26-38. </em><em>Oldenbourg Verlag, </em><em>M&#252;nchen, </em>(<em>2008</em>)Fri Jul 04 15:05:56 CEST 2008MünchenWeb 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software26-38Social Bookmarking2008bookmarking folksonomy social http://www.bibsonomy.org/bibtex/2e0743ca2e566e0a2745884f9a2189dbd/stummestumme2008-07-01T09:41:23+02:00small world smallworld <span style="color:#555555;">Brian <a href="http://www.bibsonomy.org/author/Uzzi">Uzzi</a> and Jarett <a href="http://www.bibsonomy.org/author/Spiro">Spiro</a> </span><em>American Journal of Sociology</em><em>111(2):447--504</em>(<em>2005</em>)Tue Jul 01 09:41:23 CEST 2008American Journal of Sociology2447--504Collaboration and Creativity: The Small World Problem1112005small world smallworld Small world networks have received disproportionate notice in diverse fields because of their suspected effect on system dynamics. The authors analyzed the small world network of the creative artists who made Broadway musicals from 1945 to 1989. Using original arguments, new statistical methods, and tests of construct validity, they found that the varying “small world” properties of the systemic level network of these artists affected their creativity in terms of the financial and artistic performance of the musicals they produced. The small world network effect was parabolic; performance increased up to a threshold, after which point the positive effects reversed.TRIAS - An Algorithm for Mining Iceberg Tri-Latticeshttp://www.bibsonomy.org/bibtex/2797d40e05a48f4343d7695dac87b5870/jaeschkejaeschke2008-06-23T12:23:48+02:00trias 2006 myown <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Bernhard <a href="http://www.bibsonomy.org/author/Ganter">Ganter</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>ICDM '06: Proceedings of the Sixth International Conference on Data Mining, </em><em>page907--911. </em><em>Washington, DC, USA, </em><em>IEEE Computer Society, </em>(<em>2006</em>)Mon Jun 23 12:23:48 CEST 2008Washington, DC, USAICDM '06: Proceedings of the Sixth International Conference on Data Mining907--911TRIAS - An Algorithm for Mining Iceberg Tri-Lattices2006trias 2006 myown In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed itemsets to three-dimensional data to allow for mining folk-sonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.http://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/jaeschkejaeschke2008-06-20T14:42:46+02:00logsonomy search engine network social retrieval wp5 for:nepomuk analysis l3s information <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Fri Jun 20 14:42:46 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008logsonomy search engine network social retrieval wp5 for:nepomuk analysis l3s information Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/jaeschkejaeschke2008-06-20T14:39:26+02:002008 wp5 l3s engine search for:nepomuk logsonomy myown folksonomy <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008), </em><em>AAAI Press, </em>(<em>2008</em>)Fri Jun 20 14:39:26 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy20082008 wp5 l3s engine search for:nepomuk logsonomy myown folksonomy In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.Bubble sorthttp://www.bibsonomy.org/bibtex/268e8985978a6c6abd57b7bbef5740d59/jaeschkejaeschke2008-06-18T16:18:15+02:00algorithm bubblesort sorting history <span style="color:#555555;">Owen <a href="http://www.bibsonomy.org/author/Astrachan">Astrachan</a> </span><em>SIGCSE Bull.</em><em>35(1):1--5</em>(<em>2003</em>)Wed Jun 18 16:18:15 CEST 2008New York, NY, USASIGCSE Bull.11--5Bubble sort: an archaeological algorithmic analysis352003algorithm bubblesort sorting history Text books, including books for general audiences, invariably mention bubble sort in discussions of elementary sorting algorithms. We trace the history of bubble sort, its popularity, and its endurance in the face of pedagogical assertions that code and algorithmic examples used in early courses should be of high quality and adhere to established best practices. This paper is more an historical analysis than a philosophical treatise for the exclusion of bubble sort from books and courses. However, sentiments for exclusion are supported by Knuth [17], "In short, the bubble sort seems to have nothing to recommend it, except a catchy name and the fact that it leads to some interesting theoretical problems." Although bubble sort may not be a best practice sort, perhaps the weight of history is more than enough to compensate and provide for its longevity.http://www.bibsonomy.org/bibtex/2a333df6fdc7ff9322e3ce03988a7965e/stummestumme2008-06-10T22:32:17+02:00tags schmitz folkrank stumme association bibsonomy rules tagora pagerank BibSonomy folksonomy semantik nepomuk semantics tagging 2006 UniK tagorapub hotho jaeschke folksonomies <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Social Software in der Wertsch&#246;pfung, </em><em>Baden-Baden, </em><em>Nomos, </em>(<em>2006</em>)Tue Jun 10 22:32:17 CEST 2008Baden-BadenSocial Software in der WertschöpfungDas Entstehen von Semantik in BibSonomy2006tags schmitz folkrank stumme association bibsonomy rules tagora pagerank BibSonomy folksonomy semantik nepomuk semantics tagging 2006 UniK tagorapub hotho jaeschke folksonomies Immer mehr Soziale-Lesezeichen-Systeme entstehen im heutigen Web. In solchen Systemen erstellen die Nutzer leichtgewichtige begriffliche Strukturen, so genannte Folksonomies. Ihren Erfolg verdanken sie der Tatsache, dass man keine speziellen Fähigkeiten benötigt, um an der Gestaltung mitzuwirken. In diesem Artikel beschreiben wir unser System BibSonomy. Es erlaubt das Speichern, Verwalten und Austauschen sowohl von Lesezeichen (Bookmarks) als auch von Literaturreferenzen in Form von BibTeX-Einträgen. Die Entwicklung des verwendeten Vokabulars und der damit einhergehenden Entstehung einer gemeinsamen Semantik wird detailliert diskutiert.http://www.bibsonomy.org/bibtex/203d349d70b578ca9ac3155f661151868/stummestumme2008-06-09T21:10:50+02:00web2.0 tagorapub web 2008 social myown 2.0 tagger systems folksonomy spam folksonomies bookmarking <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web, </em>(<em>2008</em>)Mon Jun 09 21:10:50 CEST 2008Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems2008web2.0 tagorapub web 2008 social myown 2.0 tagger systems folksonomy spam folksonomies bookmarking http://www.bibsonomy.org/bibtex/23b0aca61b24e4343bd80390614e3066e/stummestumme2008-06-09T14:05:11+02:00web folksonomies web2.0 2.0 collaborative semantic tagging folksonomy systems 2008 myown <span style="color:#555555;">Ciro <a href="http://www.bibsonomy.org/author/Cattuto">Cattuto</a> and Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3), </em><em>Patras, Greece, </em><em>July2008. </em>Mon Jun 09 14:05:11 CEST 2008Patras, GreeceProceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)JulySemantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems2008web folksonomies web2.0 2.0 collaborative semantic tagging folksonomy systems 2008 myown Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.http://www.bibsonomy.org/bibtex/2e053389338dafde2946ec585bc35a48e/stummestumme2008-06-09T12:37:50+02:00web introduction ml mining ir 2007 myown ki <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> (eds.). </span><em>3, </em>(<em>2007</em>)Mon Jun 09 12:37:50 CEST 2008Künstliche Intelligenz35-8Themenheft Web Mining2007web introduction ml mining ir 2007 myown ki http://www.bibsonomy.org/bibtex/2e9535ec82afa53f44a1b37704aa9a71f/stummestumme2008-06-09T12:36:33+02:00myown introduction 2007 web ir mining ml ki <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>K&#252;nstliche Intelligenz</em>(<em>2007</em>)Mon Jun 09 12:36:33 CEST 2008Künstliche Intelligenz35-8Mining the World Wide Web -- Methods, Ap- plications, and Perspectives2007myown introduction 2007 web ir mining ml ki http://www.bibsonomy.org/bibtex/26d5188d66564fe4ed7386e28868504de/stummestumme2008-06-09T12:32:10+02:00bookmark conceptual folksonomy myown social Social tagging collaborative tagorapub 2007 clustering bookmarking folksonomies <span style="color:#555555;">Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivit&#228;t (LWA 2007), </em><em>page50-54. </em><em>Martin-Luther-Universit&#228;t Halle-Wittenberg, </em><em>sep2007. </em>Mon Jun 09 12:32:10 CEST 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites2007bookmark conceptual folksonomy myown social Social tagging collaborative tagorapub 2007 clustering bookmarking folksonomies http://www.bibsonomy.org/bibtex/27fab7108fc3cf9c3f7bc4e81846969f9/stummestumme2008-05-27T12:41:01+02:00syntagms paradigmatic folksonomy syntagmatic paradigms folksonomies <span style="color:#555555;">Isabella <a href="http://www.bibsonomy.org/author/Peters">Peters</a> and Katrin <a href="http://www.bibsonomy.org/author/Weller">Weller</a> </span><em>Information Wissenschaft und Praxis</em><em>59(2):100-107</em>(<em>2008</em>)Tue May 27 12:41:01 CEST 2008Information Wissenschaft und Praxis2100-107Paradigmatic and Syntagmatic Relations in Knowledge Organization Systems592008syntagms paradigmatic folksonomy syntagmatic paradigms folksonomies http://www.bibsonomy.org/bibtex/2ce8d5ffe96977fd45bd01d677e9cc17d/stummestumme2008-05-27T09:42:10+02:00power fitting law powerlaw distribution <span style="color:#555555;">M. L. <a href="http://www.bibsonomy.org/author/Goldstein">Goldstein</a> and S. A. <a href="http://www.bibsonomy.org/author/Morris">Morris</a> and G. G. <a href="http://www.bibsonomy.org/author/Yen">Yen</a> </span><em>The European Physical Journal B - Condensed Matter and Complex Systems</em><em>41(2):255-258</em>(<em>2004</em>)Tue May 27 09:42:10 CEST 2008The European Physical Journal B - Condensed Matter and Complex Systems2255-258Fitting to the power-law distribution412004power fitting law powerlaw distribution Version 1 of Goldstein 04 power law fit containing also the chi 2 testSpringerLink - Zeitschriftenbeitraghttp://www.bibsonomy.org/bibtex/2a53905954aeef0a80ec7424f978bca14/stummestumme2008-05-26T11:57:05+02:00analysis retrieval concept carpineto formal fca ir information clustering <span style="color:#555555;">Claudio <a href="http://www.bibsonomy.org/author/Carpineto">Carpineto</a> and Giovanni <a href="http://www.bibsonomy.org/author/Romano">Romano</a> </span><em>Machine Learning</em><em>24(2):95--122</em><em>#aug#1996. </em>Mon May 26 11:57:05 CEST 2008Machine Learning#aug#295--122A lattice conceptual clustering system and its application to browsing retrieval241996analysis retrieval concept carpineto formal fca ir information clustering The theory of concept (or Galois) lattices provides a simple and formal approach to conceptual clustering. In this paper we present GALOIS, a system that automates and applies this theory. The algorithm utilized by GALOIS to build a concept lattice is incremental and efficient, each update being done in time at most quadratic in the number of objects in the lattice. Also, the algorithm may incorporate background information into the lattice, and through clustering, extend the scope of the theory. The application we present is concerned with information retrieval via browsing, for which we argue that concept lattices may represent major support structures. We describe a prototype user interface for browsing through the concept lattice of a document-term relation, possibly enriched with a thesaurus of terms. An experimental evaluation of the system performed on a medium-sized bibliographic database shows good retrieval performance and a significant improvement after the introduction of background knowledge. ER -BA model - Wikipedia, the free encyclopediahttp://www.bibsonomy.org/bibtex/273f0eae7189f539854c5d59bae595942/stummestumme2008-05-21T11:52:53+02:00power preferential law attachment <span style="color:#555555;">Albert-L&#225;szl&#243; <a href="http://www.bibsonomy.org/author/Barabási">Barab&#225;si</a> and R&#233;ka <a href="http://www.bibsonomy.org/author/Albert">Albert</a> </span><em>Science</em>(<em>1999</em>)Wed May 21 11:52:53 CEST 2008Science509--512Emergence of scaling in random networks2861999power preferential law attachment Analysis of topological characteristics of huge online social networking serviceshttp://www.bibsonomy.org/bibtex/2441b644b330fec7951c274a502c26e58/jaeschkejaeschke2008-05-20T17:48:35+02:00network online analysis folksonomy social <span style="color:#555555;">Yong-Yeol <a href="http://www.bibsonomy.org/author/Ahn">Ahn</a> and Seungyeop <a href="http://www.bibsonomy.org/author/Han">Han</a> and Haewoon <a href="http://www.bibsonomy.org/author/Kwak">Kwak</a> and Sue <a href="http://www.bibsonomy.org/author/Moon">Moon</a> and Hawoong <a href="http://www.bibsonomy.org/author/Jeong">Jeong</a> </span><em>Proceedings of the 16th international conference on World Wide Web, </em><em>page835--844. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Tue May 20 17:48:35 CEST 2008New York, NY, USAProceedings of the 16th international conference on World Wide Web835--844Analysis of topological characteristics of huge online social networking services2007network online analysis folksonomy social