In the age of big data, we often face the huge Volume, high Velocity, rich Variety, accuracy Veracity and high Value (5Vs) with complicated structures. Big data industrial decision making is crucial for a country or society to improve the effectiveness of leadership, which can remarkably promote scale development and industrialization. Under such circumstance of big data industrial decision assessment, the intrinsic issue involves serious indeterminacy. Single-valued neutrosophic set, depicted by truth membership, indeterminacy membership and falsity membership, is a highly effective way to grasp uncertainty. The dominating aim is to explore multiparametric similarity measure and distance measure with their properties. Then, the objective weight is computed by deviation-based method and the combination weight is presented. Moreover, we propose a novel neutrosophic decision making method based on multiparametric similarity measure with combination weight, which is stated by a big data industry decision making issue, along with the impact of various parameters on the final ranking. In the end, a comparison between the proposed algorithm and some existing neutrosophic algorithms has been built by the antilogarithm by zero problem, counter-intuitive phenomena and division by zero problem for showing their validity.

中文翻译：

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具有大数据行业评价的中智集多参数相似度新测度

在大数据时代，我们经常面临着庞大的体量、高速度、丰富的品种、准确的真实性和高价值（5Vs）结构复杂的问题。大数据产业决策对于一个国家或社会提高领导效能至关重要，可以显着促进规模发展和产业化。在这样的大数据产业决策评估环境下，内在的问题涉及到严重的不确定性。单值中智集合，由真隶属度、不确定隶属度和假隶属度描述，是一种非常有效的把握不确定性的方法。主要目的是探索多参数相似性度量和距离度量及其属性。然后，通过基于偏差的方法计算目标权重并给出组合权重。而且，我们提出了一种新的基于多参数相似性度量和组合权重的中智决策方法，该方法由大数据行业决策问题以及各种参数对最终排名的影响来说明。最后，通过零对数问题、反直觉现象和被零除问题对所提出的算法与一些现有的中智算法进行了比较，以证明它们的有效性。