BACKGROUND Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to tran...

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. The design, development and deployment of ETL processes, which is currently, performed in an ad-hoc, in house fashion, needs modeling, design and methodological foundations. Unfortunately, the resear...

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Portuguese noun phrase chunking, English base noun phrase chunking, English text chunking and Hindi text chunking. In all four tasks, ETL shows better r...

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. Literature and personal experience have guided us to conclude that the problems concerning the ETL tools are primarily problems of complexity, usability and price. To deal with these problems we prov...

This paper presents a review of the data quality problems that arise because of Extract, Transform and Load (ETL) technology in large organisations by observing the context in which the ETL is deployed. Using a case study methodology, information about the data quality problems and their context arising from deployments in six large organisations is reported. The findings indicate that ETL depl...

In this paper, we investigate the problem of providing scalability to data Extraction, Transformation, Load and Querying (ETL+Q) process of data warehouses. In general, data loading, transformation and integration are heavy tasks that are performed only periodically. Parallel architectures and mechanisms are able to optimize the ETL process by speedingup each part of the pipeline process as mor...

The data warehouse (DW) systems design involves several tasks such as defining the DW schemas and the ETL processes specifications, and these have been extensively studied and practiced for many years. However, the problems in heterogeneous data integration are still far from being resolved due to the complexity of ETL processes and the fundamental problems of data conflicts in information shar...

As Business Intelligence evolves from off-line strategic decision making to on-line operational decision making, the design of the back-end Extract-Transform-Load (ETL) processes is becoming even more complex. Many challenges arise in this new context like their optimization and modeling. In this paper, we focus on the disconnection between the IT-level view of the enterprise presented by ETL p...

In data warehousing, Extract-Transform-Load (ETL) extracts the data from data sources into a central data warehouse regularly for the support of business decision-makings. The data from transaction processing systems are featured with the high frequent changes of insertion, update, and deletion. It is challenging for ETL to propagate the changes to the data warehouse, and maintain the change hi...