Building data warehouses with semantic web data

Abstract

The Semantic Web (SW) deployment is now a realization and the amount of semantic annotations is ever increasing thanks to several initiatives that promote a change in the current Web towards the Web of Data, where the semantics of data become explicit through data representation formats and standards such as RDF/(S) and OWL. However, such initiatives have not yet been accompanied by efficient intelligent applications that can exploit the implicit semantics and thus, provide more insightful analysis. In this paper, we provide the means for efficiently analyzing and exploring large amounts of semantic data by combining the inference power from the annotation semantics with the analysis capabilities provided by OLAP-style aggregations, navigation, and reporting. We formally present how semantic data should be organized in a well-defined conceptual MD schema, so that sophisticated queries can be expressed and evaluated. Our proposal has been evaluated over a real biomedical scenario, which demonstrates the scalability and applicability of the proposed approach.

Interacting with Statistical Linked Data via OLAP Operations

“…Specific approaches regarding OLAP on Linked Data seem to have concentrated so far on multidimensional modelling from ontologies [6,[15][16][17]. For instance, Nebot et al [16] recognise the potential of OLAP to analyse RDF data, but do not provide a dedicated query engine and require a multidimensional database that needs to be updated if RDF data changes.…”

Implementation Of OLAP Semantic Web Technologies For Business Analytic System Development

“…Most of the existing work for bringing together OLAP and SW technologies focus on the scheme design issues, but our analysis work still unresolved by existing work. The common characteristics of existing approach is that these can be deal with unstructured data only and first challenge involves the automatic derivation of such mapping but researches says that related fields like schema mapping and data exchange has provided very useful results that have not been exploited yet by research on DW Design [8] [9].…”

RDF analytics

“…[16,34] propose RDF(S) vocabularies (pre-defined classes and properties) for describing relational multidimensional data in RDF; [16] also maps OLAP operations into SPARQL queries. [27] presents a semi-automated approach for deriving a RDW from an ontology. In contrast with the above, in our approach, the AnS instance is an RDF graph itself thus seamlessly preserves the heterogeneity, semantics, and ability to query the schema with the data present in RDF.…”