The extraction, transformation, and loading (ETL) process is a crucial and intricate area of
study that lies deep within the broad field of data warehousing. This specific, yet crucial …

The accuracy and relevance of Business Intelligence & Analytics (BI&A) rely on the ability to
bring high data quality to the data warehouse from both internal and external sources using …

Current tools that facilitate the extract-transform-load (ETL) process focus on ETL workflow,
not on generating meaningful semantic relationships to integrate data from multiple …

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 …

Many industries and academia have devoted a lot of effort and money to creating and/or
using good extract-transform-load (ETL) software suitable for their data analysis purposes …

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 …

Recently, the use of NoSQL databases has grown to manage unstructured data for
applications to ensure performance and scalability. However, many organizations prefer to …

Semantic technologies are at the core of Earth Observation (EO) data integration, by
providing an infrastructure based on RDF representation and ontologies. Because many EO …

Semantic web offers information for both individuals and computers to preserve large data
scale semantically and provide a meaningful content of unstructured data. It offers new …

User centricity represents a new trend in the currently flourishing service oriented computing
era. By upgrading end-users to prosumers (producer+ consumer) and involving them in the …