测量制造测试数据分析质量

A. Burkhardt, S. Berryman, Ashley Brio, S. Ferkau, Gloria Hubner, K. Lynch, Susan Mittman, Kathy Sonderer
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引用次数: 4

摘要

制造测试数据量不断增加。虽然文献中对大数据处理有广泛的关注,但对数据质量的关注较少,而对制造测试数据质量的关注更是少之又少。本文提出了一个完全自动化的测试数据质量度量,由作者开发,以促进制造测试操作的分析,从而产生一个单一的数字,用于比较跨程序和工厂的制造测试数据质量,并集中精力成本有效。自动化使程序和工厂用户能够直接看到、理解和改进他们的测试数据质量。即时改进测试数据质量,加快制造测试操作分析,减少测试操作的运行时间和总体花费。数据质量对企业有重大的财务影响。虽然制造成本模型被很好地理解了,但数据质量成本模型却没有被很好地理解(参见Eppler & Helfert bbb,他们回顾了制造成本模型,并为数据质量成本创建了分类)。Kim和Choi[3]讨论了测量数据质量成本,[4]中描述了基本的数据质量成本计算。Haug等人描述了数据质量差的成本分类,虽然他们没有提供成本计算,但他们确实定义了数据质量的最优性。Laranjeiro等人最近对数据质量差的分类进行了调查。Ge & Helfert[7]扩展了[7]中的工作,并提供了对数据质量成本的最新审查。测试数据是在[8]的数据处理上下文中专门处理的。[9], b[10]回顾了大数据质量工作。在[11]中讨论了数据质量度量,在[12]中确定了数据质量度量的需求。数据不一致的详细信息见[13],数据不一致的分类信息见[14]。在目前的工作中,制造测试数据的质量直接关系到制造测试操作分析的速度。制造测试数据质量的测量表明可以执行分析的速度,并且测试数据质量分数的增加导致了本文所述的分析速度的增加。
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Measuring Manufacturing Test Data Analysis Quality
Manufacturing test data volumes are constantly increasing. While there has been extensive focus in the literature on big data processing, less focus has existed on data quality, and considerably less focus has been placed specifically on manufacturing test data quality. This paper presents a fully automated test data quality measurement developed by the authors to facilitate analysis of manufacturing test operations, resulting in a single number used to compare manufacturing test data quality across programs and factories, and focusing effort cost-effectively. The automation enables program and factory users to see, understand, and improve their test data quality directly. Immediate improvements in test data quality speed manufacturing test operation analysis, reducing elapsed time and overall spend in test operations. Data quality has significant financial impacts to businesses [1]. While manufacturing cost models are well understood, data quality cost models are less well understood (see Eppler & Helfert [2] who review manufacturing cost models and create a taxonomy for data quality costs). Kim & Choi [3] discuss measuring data quality costs, and a rudimentary data quality cost calculation is described in [4]. Haug et al. [5] describe a classification of costs for poor data quality, and while they do not provide a cost calculation, they do define optimality for data quality. Laranjeiro et al. [6] have a recent survey of poor data quality classification. Ge & Helfert [7] extend the work in [2], and provide an updated review of data quality costs. Test data is specifically addressed in the context of data processing in [8]. Big data quality efforts are reviewed in [9], [10]. Data quality metrics are discussed in [11], and requirements for data quality metrics are identified in [12]. Data inconsistencies are detailed in [13], while categorical data inconsistencies are explained in [14]. In the current work, manufacturing test data quality is directly correlated to the speed of manufacturing test operations analysis. A measurement for manufacturing test data quality indicates the speed at which analysis can be performed, and increases in the test data quality score have precipitated increases in the speed of analysis, described herein.
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