Discovering Dynamic Classification Hierarchies in OLAP Dimensions

Kurzanzeige
dc.contributor.authorRehman, Nafees Ur
dc.contributor.authorMansmann, Svetlana
dc.contributor.authorWeiler, Andreas
dc.contributor.authorScholl, Marc H.
dc.date.accessioned2013-11-22T10:50:27Zdeu
dc.date.available2013-11-22T10:50:27Zdeu
dc.date.issued2012
dc.description.abstractThe standard approach to OLAP requires measures and dimensions of a cube to be known at the design stage. Besides, dimensions are required to be non-volatile, balanced and normalized. These constraints appear too rigid for many data sets, especially semi-structured ones, such as user-generated content in social networks and other web applications. We enrich the multidimensional analysis of such data via content-driven discovery of dimensions and classification hierarchies. Discovered elements are dynamic by nature and evolve along with the underlying data set.



We demonstrate the benefits of our approach by building a data warehouse for the public stream of the popular social network and microblogging service Twitter. Our approach allows to classify users by their activity, popularity, behavior as well as to organize messages by topic, impact, origin, method of generation, etc. Such capturing of the dynamic characteristic of the data adds more intelligence to the analysis and extends the limits of OLAP.		eng
dc.description.versionpublished
dc.identifier.citationFoundations of intelligent Systems : 20th international symposium ; proceedings, ISMIS 2012, Macau, China, December 4 - 7, 2012 / Li Chen ... (ed.). - Berlin : Springer, 2012. - S. 425-434. - (Lecture notes in computer science ; 7661 : Lecture notes in artificial intelligence). - ISBN 978-3-642-34623-1deu
dc.identifier.doi10.1007/978-3-642-34624-8_48deu
dc.identifier.ppn407327517deu
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/25238
dc.language.isoengdeu
dc.legacy.dateIssued2013-11-22deu
dc.rightsterms-of-usedeu
dc.rights.urihttps://rightsstatements.org/page/InC/1.0/deu
dc.subjectOLAPdeu
dc.subjectOLAP cubedeu
dc.subjectOLAP dimensionsdeu
dc.subjectdata miningdeu
dc.subjectdata warehousingdeu
dc.subjectmultidimensional data modeldeu
dc.subject.ddc004deu
dc.titleDiscovering Dynamic Classification Hierarchies in OLAP Dimensionseng
dc.typeINPROCEEDINGSdeu
dspace.entity.typePublication
kops.citation.bibtex
@inproceedings{Rehman2012Disco-25238,
  year={2012},
  doi={10.1007/978-3-642-34624-8_48},
  title={Discovering Dynamic Classification Hierarchies in OLAP Dimensions},
  number={7661},
  isbn={978-3-642-34623-1},
  publisher={Springer},
  address={Berlin},
  series={Lecture Notes in Computer Science},
  booktitle={Foundations of Intelligent Systems},
  pages={425--434},
  editor={Chen, Li and Felfernig, Alexander and Liu, Jiming and Raś, Zbigniew W.},
  author={Rehman, Nafees Ur and Mansmann, Svetlana and Weiler, Andreas and Scholl, Marc H.}
}
kops.citation.iso690REHMAN, Nafees Ur, Svetlana MANSMANN, Andreas WEILER, Marc H. SCHOLL, 2012. Discovering Dynamic Classification Hierarchies in OLAP Dimensions. In: CHEN, Li, ed., Alexander FELFERNIG, ed., Jiming LIU, ed., Zbigniew W. RAŚ, ed.. Foundations of Intelligent Systems. Berlin: Springer, 2012, pp. 425-434. Lecture Notes in Computer Science. 7661. ISBN 978-3-642-34623-1. Available under: doi: 10.1007/978-3-642-34624-8_48deu
kops.citation.iso690REHMAN, Nafees Ur, Svetlana MANSMANN, Andreas WEILER, Marc H. SCHOLL, 2012. Discovering Dynamic Classification Hierarchies in OLAP Dimensions. In: CHEN, Li, ed., Alexander FELFERNIG, ed., Jiming LIU, ed., Zbigniew W. RAŚ, ed.. Foundations of Intelligent Systems. Berlin: Springer, 2012, pp. 425-434. Lecture Notes in Computer Science. 7661. ISBN 978-3-642-34623-1. Available under: doi: 10.1007/978-3-642-34624-8_48eng
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    <dcterms:abstract xml:lang="eng">The standard approach to OLAP requires measures and dimensions of a cube to be known at the design stage. Besides, dimensions are required to be non-volatile, balanced and normalized. These constraints appear too rigid for many data sets, especially semi-structured ones, such as user-generated content in social networks and other web applications. We enrich the multidimensional analysis of such data via content-driven discovery of dimensions and classification hierarchies. Discovered elements are dynamic by nature and evolve along with the underlying data set.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;We demonstrate the benefits of our approach by building a data warehouse for the public stream of the popular social network and microblogging service Twitter. Our approach allows to classify users by their activity, popularity, behavior as well as to organize messages by topic, impact, origin, method of generation, etc. Such capturing of the dynamic characteristic of the data adds more intelligence to the analysis and extends the limits of OLAP.</dcterms:abstract>
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kops.identifier.nbnurn:nbn:de:bsz:352-252385deu
kops.relation.uniknProjectTitleExploration und Visualisierung großer Informationsmengen
kops.sourcefieldCHEN, Li, ed., Alexander FELFERNIG, ed., Jiming LIU, ed., Zbigniew W. RAŚ, ed.. <i>Foundations of Intelligent Systems</i>. Berlin: Springer, 2012, pp. 425-434. Lecture Notes in Computer Science. 7661. ISBN 978-3-642-34623-1. Available under: doi: 10.1007/978-3-642-34624-8_48deu
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kops.sourcefield.plainCHEN, Li, ed., Alexander FELFERNIG, ed., Jiming LIU, ed., Zbigniew W. RAŚ, ed.. Foundations of Intelligent Systems. Berlin: Springer, 2012, pp. 425-434. Lecture Notes in Computer Science. 7661. ISBN 978-3-642-34623-1. Available under: doi: 10.1007/978-3-642-34624-8_48eng
kops.submitter.emailnafees.rehman@uni-konstanz.dedeu
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source.contributor.editorChen, Li
source.contributor.editorFelfernig, Alexander
source.contributor.editorLiu, Jiming
source.contributor.editorRaś, Zbigniew W.
source.identifier.isbn978-3-642-34623-1
source.publisherSpringer
source.publisher.locationBerlin
source.relation.ispartofseriesLecture Notes in Computer Science
source.titleFoundations of Intelligent Systems

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