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首页> 外文期刊>Advanced Science Letters >Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping Towards Granular Computing
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Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping Towards Granular Computing

机译：比较研究，以衡量大学资质的质量及其对粒度计算的假设映射

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摘要

Nowadays, researchers are interested on granular computing in order to solve the big data problem. The volume of Big Scholarly Data (BSD) is rapidly growing. In order to evaluate the research performance, it’s becoming essential to evaluate the impact of BSD. Traditionally, journalshave been ranked by their journal impact factor (JIF). However, several impact evaluation methods have been used by different BSD digital systems, such as the citation analysis, G-Index, H-index, i10-index, jurnal impact (JIF), and the Eigenfactor. In this paper, a detailed study of thesedifferent impact evaluation methods is shown along with their advantages and disadvantages. From this study, we can say that although the evaluation methods appear highly correlated but they lead to large differences in BSD impact evaluation. We conclude that no one evaluation method is superiorand the present research gap is to develop standard rubrics and standard benchmarks in order to evaluate these existing methods. Furthermore, we have hypothetically modeled a new fuzzy granular approach as evolving structural fuzzy model (ESFM) which consider the concept of granular computing.Therefore, information granules exhibit the expressive and functional depiction of the global concept.
机译：如今，研究人员对粒度计算感兴趣，以解决大数据问题。大学术数据（BSD）的数量正在快速增长。为了评估研究表现，评估BSD的影响是至关重要的。传统上，期刊在他们的期刊影响因子（JIF）中排名。然而，不同的BSD数字系统使用了几种影响评估方法，例如引文分析，G折射率，H折射，I10索引，血管冲击（JIF）和实际因子。在本文中，显示了这种影响评价方法的详细研究以及它们的优缺点。从本研究中，我们可以说，尽管评估方法看起来高度相关，但它们导致BSD影响评估的巨大差异。我们得出结论，没有一种评价方法优于本研究差距是开发标准的尺码和标准基准，以评估这些现有方法。此外，我们已经假设了一种新的模糊粒度方法，作为演化结构模糊模型（ESFM），其考虑粒度计算的概念。因此，信息颗粒表现出全球概念的表现力和功能描述。

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