Semantic Web (SW) techniques, such as ontologies, are used in Information Systems (IS) to
cope with the growing need for sharing and reusing data and knowledge in various research …

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 …

The popularity of business intelligence (BI) systems to support business analytics has
tremendously increased in the last decade. The determination of data items that should be …

This paper describes the convergence of some of the most influential technologies in the last
few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the …

Self-service business intelligence is about enabling non-expert users to make well-informed
decisions by enriching the decision process with situational data, ie, data that have a narrow …

At the early stages of a data warehouse design project, the main objective is to collect the
business requirements and needs, and translate them into an appropriate conceptual …

OLAP has been extensively used for a couple of decades as a data analysis approach to
support decision making on enterprise structured data. Now, with the wide diffusion of …

Designing data warehouse (DW) systems in highly dynamic enterprise environments is not
an easy task. At each moment, the multidimensional (MD) schema needs to satisfy the set of …

In last decades, semantic databases (SDB) emerge and become operational databases,
since the major vendors provide semantic supports in their products. This is mainly due to …

Many real-world ontologies contain thousands of terms and are developed by multiple
participants. The use of monolithic ontologies can cause problems that affect various stages …