Ontology evolution aims at maintaining an ontology up to date with respect to changes in the
domain that it models or novel requirements of information systems that it enables. The …

As business intelligence becomes increasingly essential for organizations and as it evolves
from strategic to operational, the complexity of Extract-Transform-Load (ETL) processes …

There is an abundance of data, but a large volume of it is unusable. Data may be noisy,
unstructured, stored in incompatible for direct analysis medium or format, and often …

One of the problems arising upon the evolution of a database schema is that some queries
and views defined on the previous schema version might no longer work properly. Thus …

Business Intelligence (BI) applications require the design, implementation, and maintenance
of processes that extract, transform, and load suitable data for analysis. The development 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 …

One of the main tasks during the early steps of a data warehouse project is the identification
of the appropriate transformations and the specification of inter-schema map**s from the …

This paper provides simulation practitioners and interested simulation consumers with a
grounding in how discrete-event simulation software works. Topics include discrete-event …

Like all software systems, databases are subject to evolution as time passes. The impact of
this evolution can be vast as a change to the schema of a database can affect the syntactic …

Extract-Transform-Load (ETL) processes constitute the back stage of Data Warehouse
architectures. Several studies characterize the ETL design as a time-consuming and error …