Quality engineers are increasingly faced with the need to deal with new types of data, which are significantly different from ordinary numerical data by virtue of their nature and the operations that can be performed with them. Basic concepts related to processing of such data, ie, data similarity, measurement system analysis, variation analysis, and data fusion, need to be thoroughly rethought. Reviewing recent publications in the field, we suggest a common approach to processing all data types on the basis of the idea of defining the distance metric for the appropriate data space. The article discusses six types of quality data (nominal, ordinal, preference chains, strings, tree structured, and product/process distribution) and four data processing aspects (calculating data similarity, error description, data fusion, and intradispersion and interdispersion studies). Necessary information and recommendations are given for each combination of data type and problem. They are also summarized in a table that refers the reader to various sections of the article. Any other data type for which the distance metric is definable can be included into the framework of the proposed unified approach.

中文翻译：

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处理新型质量数据

质量工程师越来越需要处理新型数据，这些数据由于其性质和可以执行的操作而与普通数值数据明显不同。需要彻底考虑与此类数据处理相关的基本概念，即数据相似性，测量系统分析，变化分析和数据融合。回顾该领域的最新出版物，我们基于定义适当数据空间的距离度量的思想，提出了一种处理所有数据类型的通用方法。本文讨论了六种类型的质量数据（标称，有序，偏好链，字符串，树形结构和产品/过程分布）和四个数据处理方面（计算数据相似性，错误描述，数据融合，以及分散内和分散内研究）。针对数据类型和问题的每种组合都提供了必要的信息和建议。它们还汇总在表格中，使读者可以参考本文的各个部分。可以为距离度量定义的任何其他数据类型都可以包含在建议的统一方法的框架中。