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dc.contributor.authorHernansaez, Juan Manuelspa
dc.contributor.authorBotía, Juan A.spa
dc.contributor.authorSkarmeta, Antonio F.spa
dc.date.accessioned2020-10-27T00:21:19Z
dc.date.available2020-10-27T00:21:19Z
dc.date.issued2004-06-01
dc.identifier.issn2539-2115
dc.identifier.issn1657-2831
dc.identifier.urihttp://hdl.handle.net/20.500.12749/9039
dc.description.abstractIn this paper, we discuss the most important aspects of METALA, a software tool for meta-learning that we have developed to perform inductive learning in a distributed and component based fashion. The distribution comes from the use of a well posed distributed application development standard as is J2EE, and the component basis comes from the methodology we developed to integrate new learning algorithms and other software utilities into the system.Keywords: Software architecture, web usage mining, inductive learning, knowledge models, J2EE, XML.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNAB
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/1080/1052
dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/article/view/1080
dc.rightsDerechos de autor 2004 Revista Colombiana de Computación
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.sourceRevista Colombiana de Computación; Vol. 5 Núm. 1 (2004): Revista Colombiana de Computación; 1-12
dc.subjectInnovaciones tecnológicas
dc.subjectCiencia de los computadores
dc.subjectDesarrollo de tecnología
dc.subjectIngeniería de sistemas
dc.subjectInvestigaciones
dc.subjectTecnologías de la información y las comunicaciones
dc.subjectTIC´s
dc.titleMETALA: un marco basado en tecnología J2EE para minería webspa
dc.title.translatedMETALA: a J2EE technology based framework for web miningeng
dc.type.driverinfo:eu-repo/semantics/article
dc.type.localArtículospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsTechnological innovationseng
dc.subject.keywordsComputer scienceeng
dc.subject.keywordsTechnology developmenteng
dc.subject.keywordsSystems engineeringeng
dc.subject.keywordsInvestigationseng
dc.subject.keywordsInformation and communication technologieseng
dc.subject.keywordsICT'seng
dc.subject.keywordsSoftware architectureeng
dc.subject.keywordsWeb usage miningeng
dc.subject.keywordsInductive learningeng
dc.subject.keywordsKnowledge modelseng
dc.subject.keywordsJ2EEeng
dc.subject.keywordsXMLeng
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.type.hasversionInfo:eu-repo/semantics/publishedVersion
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.relation.referencesR. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pages 487–499. Morgan Kaufmann, 12–15 1994.
dc.relation.referencesJ.A. Botia, A.G. Skarmeta, M. Valdes, and A. Padilla. Metala: A meta-learning architecture. In Proceeding of the 7th Fuzzy Days, Dortmund, Germany, October 2001.
dc.relation.referencesS. Brantschen and T. Haas. Agents in a j2ee world. Technical report, Whitestein Technologies AG, 2002.
dc.relation.referencesM-S. Chen, J.S. Park, and P.S. Yu. Efficient data mining for path traversal patterns. Knowledge and Data Engineering, 10(2):209–221, 1998.
dc.relation.referencesR. Cooley, B. Mobasher, and J. Srivastava. Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1(1):5–32, 1999.
dc.relation.referencesR. Cooley, P. Tan, and J. Srivastava. Websift: the web site information filter system, 1999.
dc.relation.referencesR. Cooley, P-N. Tan, and J. Srivastava. Discovery of interesting usage patterns from web data. In WEBKDD, pages 163–182, 1999.
dc.relation.referencesGlobal Grid Forum. Open grid services infrastructure (ogsi) version 1.0, 2003.
dc.relation.referencesA. Joshi and R. Krishnapuram. On mining web access logs. In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 63–69, 2000.
dc.relation.referencesB. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on Web usage mining. Communications of the ACM, 43(8):142–151, 2000.
dc.relation.referencesK-L. Ong, W-K. Ng, and E-P. Lim. A web mining platform for enhancing knowledge management on the web. In Proc. of Int. Workshop on Integrating Data Mining and Knowledge Management (in conj. with 1st IEEE Int. Conf. on Data Mining), San Jose, California, 2001.
dc.relation.referencesS.K. Pal, V. Talwar, and P. Mitra. Web mining in soft computing framework: Relevance, state of the art and future directions. In IEEE Transactions on Neural Networks, volume 13 of 5, pages 1163–1177, 2002.
dc.relation.referencesM. Spiliopoulou. Web usage mining for web site evaluation. Commun. ACM, 43(8):127– 134, 2000.
dc.relation.referencesM. J. Wooldridge. Introduction to Multiagent Systems. John Wiley and Sons, New York, USA, 2002.
dc.relation.referencesJ. Xu, Y. Huang, and G. Madey. A research support system framework for web data mining. In J. T. Yao and P. Lingras, editors, WI/IAT 2003 Workshop on Applications, Products and Services of Web-based Support Systems, Halifax, Canada, 2003.
dc.contributor.googlescholarBotía, Juan A. [IFiSPE4AAAAJ]spa
dc.contributor.orcidBotía, Juan A. [0000-0002-6992-598X]spa
dc.contributor.orcidSkarmeta, Antonio F. [0000-0002-5525-1259]spa
dc.contributor.researchgateBotía, Juan A. [Juan-Botia]spa
dc.subject.lembDesarrollo tecnológicospa
dc.subject.lembInnovaciones tecnológicasspa
dc.subject.lembCiencias de la computaciónspa
dc.subject.lembInvestigaciónspa
dc.subject.lembTecnología de la información y la comunicaciónspa
dc.identifier.repourlrepourl:https://repository.unab.edu.co
dc.description.abstractenglishIn this paper, we discuss the most important aspects of METALA, a software tool for meta-learning that we have developed to perform inductive learning in a distributed and component based fashion. The distribution comes from the use of a well posed distributed application development standard as is J2EE, and the component basis comes from the methodology we developed to integrate new learning algorithms and other software utilities into the system. We aim to use this new architecture for evaluate algorithms of Web Usage Mining, a concrete learning problem of the Web Mining area, and for taking advantage of these algorithms to build new knowledge models. These models can be used then to create and incorporate new applications and tools to the architecture. We discuss the advantages and flaws of using J2EE as the technology basis. We also compare our architecture with some other software platforms intended to solve similar Web Mining problems as METALA can solve. To illustrate the use of METALA, we present a complete Web Usage Mining life cycle process explanation.eng
dc.subject.proposalArquitectura del softwarespa
dc.subject.proposalMinería de uso de la webspa
dc.subject.proposalAprendizaje inductivospa
dc.subject.proposalModelos de conocimientospa
dc.subject.proposalJ2EEspa
dc.subject.proposalXMLspa
dc.type.redcolhttp://purl.org/redcol/resource_type/CJournalArticle
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*


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