We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.

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In many scenarios, such as the ones related to Data Warehousing Extract-Transform-Load (ETL) processes, logging techniques are usually applied for capturing event metrics across system levels for system auditing and system recovery. The diversity of strategies and architectures of the toolset used to support the ETL implementation introduces another layer of complexity, both for system development and audit. Although a valuable system diagnosis resource for the development team, logging is generally underestimated, being used only when the system reveals unexpected behaviours and not to drive the ETL system evolution. We believe that the use of logs for steering ETL development and maintenance can improve significantly global system quality. However, this approach is only effective if flexible and efficient logging systems exist. In this paper, we describe a Log Pattern used in a pattern-oriented approach for ETL systems development, which provides a configurable and flexible component for using to drive ETL development and maintenance phases.