Interactive multidimensional modeling of linked data for exploratory OLAP

作者：

Highlights：

• Exploratory OLAP blends OLAP and the Semantic Web to enable cross-domain analyses by adopting a publish-enrich-query paradigm.

• While some approaches were devised for the public and query stages, the enrich stage has not been investigated yet.

• We propose the iMOLD approach, that enables data enthusiasts to enrich RDF cubes with aggregation hierarchies by exploring linked data.

• This is done by detecting five recurring modeling patterns that express roll-up relationships between RDF concepts.

• A case study based on DBpedia is proposed and the results of an evaluation test made with real users are discussed.

摘要

•Exploratory OLAP blends OLAP and the Semantic Web to enable cross-domain analyses by adopting a publish-enrich-query paradigm.•While some approaches were devised for the public and query stages, the enrich stage has not been investigated yet.•We propose the iMOLD approach, that enables data enthusiasts to enrich RDF cubes with aggregation hierarchies by exploring linked data.•This is done by detecting five recurring modeling patterns that express roll-up relationships between RDF concepts.•A case study based on DBpedia is proposed and the results of an evaluation test made with real users are discussed.

论文关键词：Multidimensional modeling,Data warehouse design,Linked data,Exploratory OLAP

论文评审过程：Received 29 November 2016, Revised 17 November 2017, Accepted 4 June 2018, Available online 5 June 2018, Version of Record 18 June 2018.

论文官网地址：https://doi.org/10.1016/j.is.2018.06.004