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 ETL (Extract-Transform-Load) processes are responsible for integrating data into a
place called datawarehouse. In the ETL phase, data are extracted from various sources …

The software processes that facilitate the original loading and the periodic refreshment of the
data warehouse contents are commonly known as Extraction-Transformation-Loading (ETL) …

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 …

Big Data has become the new ubiquitous term used to describe massive collection of
datasets that are difficult to process using traditional database and software techniques …

In this paper, we discuss the state of the art and current trends in designing and optimizing
ETL workflows. We explain the existing techniques for:(1) constructing a conceptual and a …

In the last decade, a large number of knowledge graph (KG) completion approaches were
proposed. Albeit effective, these efforts are disjoint, and their collective strengths and …

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

As discussed in section 2.1 of this thesis we regard semantic technologies “as technical
approaches that facilitate or make use of the interpretation of meaning by machines” …

The data warehouse design task needs to consider both the end-user requirements and the
organization data sources. For this reason, the data warehouse design has been …