Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of schema matching: the process of establishing correspondences between datasets. Traditionally, schema matching has been used to find matching pairs of columns between a source and a target schema. However, the use of schema matching in dataset discovery methods differs from its original use. Nowadays schema matching serves as a building block for indicating and ranking inter-dataset relationships. Surprisingly, although a discovery method’s success relies highly on the quality of the underlying matching algorithms, the latest discovery methods employ existing schema matching algorithms in an ad-hoc fashion due to the lack of openly-available datasets with ground truth, reference method implementations, and evaluation metrics. In this paper, we aim to rectify the problem of evaluating the effectiveness and efficiency of schema matching methods for the specific needs of dataset discovery. To this end, we propose Valentine, an extensible open-source experiment suite to execute and organize large-scale automated matching experiments on tabular data. Valentine includes implementations of seminal schema matching methods that we either implemented from scratch (due to absence of open source code) or imported from open repositories. The contributions of Valentine are: i) the definition of four schema matching scenarios as encountered in dataset discovery methods, ii) a principled dataset fabrication process tailored to the scope of dataset discovery methods and iii) the most comprehensive evaluation of schema matching techniques to date, offering insight on the strengths and weaknesses of existing techniques, that can serve as a guide for employing schema matching in future dataset discovery methods. @en

A numerical computer simulation has been developed for the electrostatic spray paint process. The theoretical model describes the path of a cloud of charged drops. It takes into account the drop size, temperature, density, viscosity of paint and air, charge of the drops, potential of the sprayer head, the use of air assistance, air-paint ratio, paint flow, wind and turbulence and the distance towards the object. Especially the influence of the wind and wind turbulence on the spraying process was investigated. With increasing crosswind a higher charge, larger drops or air-assisted spraying will keep the efficiency high. Too much charge on the drops will cause the cloud to expand too much. Painting in the open air with a mild wind is possible using the electrostatic spray paint process.@en