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As business requirements evolve with increasing information density and velocity, there is a growing need for efficiency and automation of Extract-Transform-Load (ETL) processes. Current approaches for the modeling and optimization of ETL processes provide platform-independent optimization solutions for the (semi-)automated transition among different(More)
Obtaining the right set of data for evaluating the fulfillment of different quality standards in the extract-transform-load (ETL) process design is rather challenging. First, the real data might be out of reach due to different privacy constraints, while providing a synthetic set of data is known as a labor-intensive task that needs to take various(More)
Unfortunately, the original version of this supplement [1] contained errors in two of the abstracts; P037 and P127. Please see details below. In P037, the image presented as Figure eight is incorrect. The correct figure is shown below (Fig. 1). permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to(More)
We present a tool, called POIESIS, for automatic ETL process enhancement. ETL processes are essential data-centric activities in modern business intelligence environments and they need to be examined through a viewpoint that concerns their quality characteristics (e.g., data quality, performance, manageability) in the era of Big Data. POIESIS responds to(More)
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