Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and size of modern data sets, the necessity for the development of understandable and efficient scaling methods is at hand. A profound theory for scaling data is scale-measures, as developed in the field of formal concept analysis. Recent developments indicate that the set of all scale-measures for a given data set constitutes a lattice and does hence allow efficient exploring algorithms. In this work we study the properties of said lattice and propose a novel scale-measure exploration algorithm that is based on the well-known and proven attribute exploration approach. Our results motivate multiple applications in scale recommendation, most prominently (semi-)automatic scaling.

翻译：计量是众多科学模型及其创建的基本基石。对于数据驱动的科学来说,尤其如此。由于现代数据集的高度复杂性和规模,发展可理解和高效的缩放方法的必要性即将到来。数据缩放的深刻理论是正式概念分析领域开发的尺度测量方法。最近的事态发展表明,特定数据集的所有尺度测量方法都是一个细小的,因此允许有效探索算法。在这项工作中,我们研究了上述拉蒂斯的特性,并提出了基于众所周知和经证实的属性探索方法的新型比例尺度测量勘探算法。我们的结果激励了在规模建议中的多种应用,最显著的是(半)自动缩放法。