The extract, transform, and load (ETL) process is at the core of data warehousing
architectures. As such, the success of data warehouse (DW) projects is essentially based on …

Data analytics plays a vital role in contemporary organizations, through analytics,
organizations are able to derive knowledge and intelligence from data to support strategic …

Many clinical trials and scientific studies have been conducted aiming for better
understanding of specific medical conditions. However, these studies are often based on a …

In order to create better decisions for business analytics, organizations increasingly use
external structured, semi-structured, and unstructured data in addition to the (mostly …

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 …

The popularity of the Semantic Web (SW) encourages organizations to organize and publish
semantic data using the RDF model. This growth poses new requirements to Business …

With the growing popularity of Semantic Web technologies, more and more organizations
natively manage data using Semantic Web standards, in particular RDF. This development …

Large volumes of geospatial data are being published on the Semantic Web (SW), yielding
a need for advanced analysis of such data. However, existing SW technologies only support …

Abstract The arrival of Big Data has contributed positively to the evolution of the data
warehouse (DW) technology. This gives birth of augmented DW s that aim at maximizing the …

There has been an exponential growth and availability of data, both structured and
unstructured. Massive amounts of data are available to be harvested for competitive …