Interactive multidimensional modeling of linked data for exploratory OLAP
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hdl:2117/119270
Tipus de documentArticle
Data publicació2018-09
EditorElsevier
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Exploratory OLAP aims at coupling the precision and detail of corporate data with the information wealth of LOD. While some techniques to create, publish, and query RDF cubes are already available, little has been said about how to contextualize these cubes with situational data in an on-demand fashion. In this paper we describe an approach, called iMOLD, that enables non-technical users to enrich an RDF cube with multidimensional knowledge by discovering aggregation hierarchies in LOD. This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll-up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube. Two families of aggregation patterns are identified, based on associations and generalization respectively, and the algorithms for recognizing them are described. To evaluate iMOLD in terms of efficiency and effectiveness we compare it with a related approach in the literature, we propose a case study based on DBpedia, and we discuss the results of a test made with real users.
CitacióGallinucci, E., Golfarelli, M., Rizzi, S., Abelló, A., Romero, O. Interactive multidimensional modeling of linked data for exploratory OLAP. "Information systems", Setembre 2018, vol. 77, p. 86-104.
ISSN0306-4379
Versió de l'editorhttps://www.sciencedirect.com/science/article/pii/S0306437916305956
Col·leccions
- inSSIDE - integrated Software, Service, Information and Data Engineering - Articles de revista [113]
- Departament d'Enginyeria de Serveis i Sistemes d'Informació - Articles de revista [222]
- GESSI - Grup d'Enginyeria del Software i dels Serveis - Articles de revista [56]
- IMP - Information Modeling and Processing - Articles de revista [126]
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