Scalable methods to analyze Semantic Web data
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Scalable methods to analyze Semantic Web dataAutoria
Data de publicació
2016-04-26Editor
IOS PressCita bibliogràfica
NEBOT ROMERO, María Victoria. Scalable methods to analyze Semantic Web data. AI Communications (2016), v. 29, n. 3. pp. 473-475Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://content.iospress.com/articles/ai-communications/aic669?resultNumber=1&tot ...Versió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
Semantic Web data is currently being heavily used as a data representation format in scientific communities, social networks, business companies, news portals and other domains. The irruption and availability of ... [+]
Semantic Web data is currently being heavily used as a data representation format in scientific communities, social networks, business companies, news portals and other domains. The irruption and availability of Semantic Web data is demanding new methods and tools to efficiently analyze such data and take advantage of the underlying semantics. Although there exist some applications that make use of Semantic Web data, advanced analytical tools are still lacking, preventing the user from exploiting the attached semantics. The main objective of this dissertation is to provide a formal framework that enables the multidimensional analysis of Semantic Web data in a scalable and efficient manner. The success of multidimensional analysis techniques applied to large volumes of structured data in the context of business intelligence, especially for data warehousing and OLAP applications, has prompted us to investigate the application of such techniques to Semantic Web data, whose nature is semi-structured and contain implicit knowledge. [-]
Publicat a
AI Communications (2016), v. 29, n. 3.Drets d'accés
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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