Discovering OLAP Dimensions in Semi-Structured Data

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2014
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Information Systems. 2014, 44, pp. 120-133. ISSN 0306-4379. eISSN 0306-4379. Available under: doi: 10.1016/j.is.2013.09.002
Zusammenfassung

OLAP cubes enable aggregation-centric analysis of transactional data by shaping data records into measurable facts with dimensional characteristics. A multidimensional view is obtained from the available data fields and explicit relationships between them. This classical modeling approach is not feasible for scenarios dealing with semi-structured or poorly structured data. We propose to the data warehouse design methodology with a content-driven discovery of measures and dimensions in the original dataset. Our approach is based on introducing a data enrichment layer responsible for detecting new structural elements in the data using data mining and other techniques. Discovered elements can be of type measure, dimension, or hierarchy level and may represent static or even dynamic properties of the data. This paper focuses on the challenge of generating, maintaining, and querying discovered elements in OLAP cubes.



We demonstrate the power of our approach by providing OLAP to the public stream of user-generated content on the Twitter platform. We have been able to enrich the original set with dynamic characteristics, such as user activity, popularity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional models from relational or semi-structured data.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Data warehousing, OLAP, Multidimensional data model, Semi-structured data
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690MANSMANN, Svetlana, Nafees Ur REHMAN, Andreas WEILER, Marc H. SCHOLL, 2014. Discovering OLAP Dimensions in Semi-Structured Data. In: Information Systems. 2014, 44, pp. 120-133. ISSN 0306-4379. eISSN 0306-4379. Available under: doi: 10.1016/j.is.2013.09.002
BibTex
@article{Mansmann2014Disco-25828,
  year={2014},
  doi={10.1016/j.is.2013.09.002},
  title={Discovering OLAP Dimensions in Semi-Structured Data},
  volume={44},
  issn={0306-4379},
  journal={Information Systems},
  pages={120--133},
  author={Mansmann, Svetlana and Rehman, Nafees Ur and Weiler, Andreas and Scholl, Marc H.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/25828">
    <dcterms:title>Discovering OLAP Dimensions in Semi-Structured Data</dcterms:title>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/25828"/>
    <dc:contributor>Rehman, Nafees Ur</dc:contributor>
    <dc:creator>Scholl, Marc H.</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/25828/2/Mansmann_258286.pdf"/>
    <dc:creator>Mansmann, Svetlana</dc:creator>
    <dcterms:abstract xml:lang="eng">OLAP cubes enable aggregation-centric analysis of transactional data by shaping data records into measurable facts with dimensional characteristics. A multidimensional view is obtained from the available data fields and explicit relationships between them. This classical modeling approach is not feasible for scenarios dealing with semi-structured or poorly structured data. We propose to the data warehouse design methodology with a content-driven discovery of measures and dimensions in the original dataset. Our approach is based on introducing a data enrichment layer responsible for detecting new structural elements in the data using data mining and other techniques. Discovered elements can be of type measure, dimension, or hierarchy level and may represent static or even dynamic properties of the data. This paper focuses on the challenge of generating, maintaining, and querying discovered elements in OLAP cubes.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;We demonstrate the power of our approach by providing OLAP to the public stream of user-generated content on the Twitter platform. We have been able to enrich the original set with dynamic characteristics, such as user activity, popularity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional models from relational or semi-structured data.</dcterms:abstract>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/25828/2/Mansmann_258286.pdf"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Weiler, Andreas</dc:creator>
    <dc:creator>Rehman, Nafees Ur</dc:creator>
    <dc:contributor>Scholl, Marc H.</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Mansmann, Svetlana</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-13T13:48:42Z</dcterms:available>
    <dcterms:issued>2014</dcterms:issued>
    <dc:rights>terms-of-use</dc:rights>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-13T13:48:42Z</dc:date>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Weiler, Andreas</dc:contributor>
    <dcterms:bibliographicCitation>Information Systems ; 44 (2014). - S. 120-133</dcterms:bibliographicCitation>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen