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

Smart city data come from heterogeneous sources including various types of the Internet of
Things such as traffic, weather, pollution, noise, and portable devices. They are …

ETL processes are the backbone component of a data warehouse, since they supply the
data warehouse with the necessary integrated and reconciled data from heterogeneous and …

The ETL (Extract-Transform-Load) consists of extracting data from various sources,
transforming and loading them into a place called datawarehouse. ETL is a mandatory step …

Among the so-called “4Vs”(volume, velocity, variety, and veracity) that characterize the
complexity of Big Data, this paper focuses on the issue of “Volume” in order to ensure good …

Extract-Transform-Load (ETL) programs process data into data warehouses (DWs). Rapidly
growing data volumes demand systems that scale out. Recently, much attention has been …

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 …

The tremendous popularity of web-based social media is attracting the attention of the
industry to take profit from the massive availability of sentiment data, which is considered of …

Abstract Extract-Transform-Load (ETL) flows periodically populate data warehouses (DWs)
with data from different source systems. An increasing challenge for ETL flows is processing …

This paper demonstrates ETLMR, a novel dimensional Extract--Transform--Load (ETL)
programming framework that uses Map-Reduce to achieve scalability. ETLMR has built-in …