granularity

简明释义

英[ˌɡrænjəˈlærəti]美[ˈɡrænjʊˈlerəti]

n. 间隔尺寸，[岩] 粒度

英英释义

单词用法

同义词

反义词

例句

1.This brings up the question of granularity, which will be addressed later.

这将会产生粒度问题，后面我们将对此进行讨论。

2.The granularity is days.

粒度是天。

3.Thus, the granularity of versioning here may become too large.

因此，这种应用程序版本的粒度太大。

4.The distinction is made merely to introduce the important concept of granularity.

做出这样的区别仅仅是为了引入粒度这个重要的概念。

5.Find the right granularity for pattern design.

寻找出模式设计的合适粒度。

6.Different extent sizes means different VG granularity.

不同的区段大小意味着不同的vg粒度。

7.The data analysis showed a high level of granularity in the sales figures, allowing us to identify specific trends.

数据分析显示销售数字的粒度很高，使我们能够识别出具体的趋势。

8.In project management, adjusting the granularity of tasks can help in better resource allocation.

在项目管理中，调整任务的粒度可以帮助更好地分配资源。

9.The granularity of the financial report was too broad, making it difficult to pinpoint areas for improvement.

财务报告的粒度太宽泛，难以找出改进的领域。

10.By increasing the granularity of our customer segmentation, we can tailor our marketing strategies more effectively.

通过提高客户细分的粒度，我们可以更有效地定制我们的营销策略。

11.The software allows users to adjust the granularity of the data displayed on their dashboards.

该软件允许用户调整其仪表板上显示的数据的粒度。

作文

In recent years, the concept of granularity has gained significant attention across various fields, ranging from data analysis to software development. But what exactly does granularity mean? In simple terms, granularity refers to the level of detail or precision that is captured in a dataset or system. Understanding granularity is crucial because it can greatly influence the outcomes of any analysis or decision-making process. For instance, in data analysis, the granularity of the data can determine how insights are derived. If a dataset is too coarse, it may obscure important trends or patterns. Conversely, if the granularity is too fine, it may lead to information overload, making it challenging to extract meaningful conclusions. Therefore, finding the right balance in granularity is essential for effective data interpretation. In the realm of software development, granularity plays a vital role in defining the components of a system. A highly granular approach involves breaking down functionalities into smaller, more manageable parts. This can enhance modularity and reusability, allowing developers to create systems that are easier to maintain and update. However, excessive granularity can also introduce complexity, making it difficult to manage interactions between components. Moreover, granularity is not limited to technical fields; it also extends to everyday decision-making. For example, when planning a project, one must consider the granularity of the tasks involved. A detailed breakdown of tasks can help ensure that all aspects of the project are addressed, while a high-level overview may suffice for simpler projects. Thus, understanding the appropriate level of granularity is key to successful project management. Another area where granularity is significant is in risk assessment. When evaluating potential risks, the granularity of the analysis can dictate how well risks are identified and mitigated. A detailed risk assessment with fine granularity allows for a better understanding of specific vulnerabilities, while a broader assessment might overlook critical issues. Therefore, the choice of granularity can have profound implications for risk management strategies. In conclusion, the concept of granularity encompasses a wide range of applications and implications. Whether in data analysis, software development, project management, or risk assessment, the level of granularity can significantly impact the effectiveness and efficiency of processes. It is essential to recognize that achieving the right level of granularity requires careful consideration of the context and objectives at hand. By doing so, individuals and organizations can make more informed decisions and derive greater value from their efforts.

近年来，‘granularity’这一概念在各个领域引起了广泛关注，从数据分析到软件开发。但‘granularity’到底意味着什么呢？简单来说，‘granularity’指的是在数据集或系统中捕获的细节或精确度的水平。理解‘granularity’至关重要，因为它会极大影响任何分析或决策过程的结果。 例如，在数据分析中，数据的‘granularity’可以决定洞察力的提取。如果数据集过于粗糙，它可能会掩盖重要的趋势或模式。相反，如果‘granularity’过于细致，可能会导致信息过载，使得提取有意义的结论变得困难。因此，在‘granularity’方面找到合适的平衡对于有效的数据解释至关重要。 在软件开发领域，‘granularity’在定义系统组件时起着重要作用。高度细化的方法涉及将功能分解为更小、更易于管理的部分。这可以增强模块化和可重用性，使开发人员能够创建更易于维护和更新的系统。然而，过度的‘granularity’也可能引入复杂性，使得管理组件之间的交互变得困难。 此外，‘granularity’不仅限于技术领域，它还扩展到日常决策中。例如，在规划项目时，必须考虑所涉及任务的‘granularity’。详细的任务细分可以帮助确保项目的各个方面都得到解决，而高层次的概述可能足以应对较简单的项目。因此，理解适当的‘granularity’是成功项目管理的关键。 另一个‘granularity’重要性的领域是风险评估。在评估潜在风险时，分析的‘granularity’可以决定风险识别和缓解的有效性。具有细致‘granularity’的风险评估可以更好地理解特定的脆弱性，而更广泛的评估可能会忽视关键问题。因此，‘granularity’的选择对风险管理策略有深远的影响。 总之，‘granularity’的概念涵盖了广泛的应用和含义。无论是在数据分析、软件开发、项目管理还是风险评估中，‘granularity’的水平都可以显著影响过程的有效性和效率。必须认识到，实现适当的‘granularity’需要仔细考虑上下文和目标。通过这样做，个人和组织可以做出更明智的决策，并从他们的努力中获得更大的价值。