amenable data

简明释义

经得嘴验的数据;

英英释义

例句

1.The software is designed to work with amenable data 可处理的数据 to ensure accurate results.

该软件旨在处理可处理的数据以确保结果的准确性。

2.In order to improve the model, we need to gather more amenable data 可处理的数据 for our analysis.

为了改善模型，我们需要收集更多的可处理的数据以供分析。

3.The research team was able to analyze the amenable data 可处理的数据 collected from the survey.

研究团队能够分析从调查中收集的可处理的数据。

4.The analysis revealed that the amenable data 可处理的数据 had significant implications for the study.

分析显示，可处理的数据对研究有重要影响。

5.Researchers are looking for amenable data 可处理的数据 that can be easily manipulated for their experiments.

研究人员正在寻找可以轻松操作的可处理的数据以进行实验。

作文

In today's data-driven world, the concept of amenable data has become increasingly significant. Amenable data refers to information that is easily adaptable or responsive to analysis and interpretation. This type of data can be manipulated or transformed for various purposes, making it a valuable asset in fields such as research, business analytics, and artificial intelligence. Understanding the nature of amenable data is crucial for anyone looking to harness the power of information effectively.One of the primary characteristics of amenable data is its structured format. Structured data usually resides in databases and is organized in a way that makes it straightforward to query and analyze. For instance, customer information stored in a relational database is considered amenable data because it can be easily accessed and manipulated to generate insights about purchasing behaviors or preferences. This accessibility allows organizations to make informed decisions based on empirical evidence rather than intuition alone.On the other hand, unstructured data poses challenges when it comes to analysis. Examples of unstructured data include social media posts, emails, and multimedia files. While this type of data may contain valuable insights, it is often not amenable data because it lacks the organization necessary for immediate analysis. However, advancements in natural language processing and machine learning are gradually making it possible to extract meaningful information from unstructured data, thereby expanding the scope of what can be classified as amenable data.The importance of amenable data cannot be overstated, especially in the context of decision-making processes. Businesses that utilize amenable data can quickly adapt to market changes, understand customer needs more profoundly, and optimize their operations. For example, a retail company that analyzes sales data can identify trends and adjust its inventory accordingly, ensuring that popular products are always in stock. This level of responsiveness is essential in today's fast-paced environment where consumer preferences can shift rapidly.Moreover, amenable data plays a critical role in scientific research. Researchers rely on data that can be easily analyzed to draw conclusions and validate hypotheses. In fields such as medicine, having access to amenable data means that scientists can quickly analyze patient records or clinical trial results, leading to faster discoveries and innovations. The ability to manipulate this data efficiently can significantly impact the pace at which new treatments and therapies are developed.In conclusion, the concept of amenable data is fundamental in our data-centric society. Its characteristics, including structure and adaptability, make it an invaluable resource for businesses, researchers, and decision-makers alike. As technology continues to evolve, the definition of what constitutes amenable data may expand, allowing us to glean insights from increasingly diverse sources of information. Embracing and understanding amenable data will undoubtedly lead to more informed decisions and better outcomes across various sectors.

在当今数据驱动的世界中，可处理数据的概念变得越来越重要。可处理数据是指可以轻松适应或响应分析和解释的信息。这种类型的数据可以被操纵或转化为各种目的，使其在研究、商业分析和人工智能等领域成为有价值的资产。理解可处理数据的性质对于任何希望有效利用信息的人来说都是至关重要的。可处理数据的主要特征之一是其结构化格式。结构化数据通常存储在数据库中，并以一种使其易于查询和分析的方式组织。例如，存储在关系数据库中的客户信息被视为可处理数据，因为可以轻松访问和操作这些数据，以生成关于购买行为或偏好的见解。这种可访问性使组织能够基于实证证据而非直觉做出明智的决策。另一方面，非结构化数据在分析时会面临挑战。非结构化数据的例子包括社交媒体帖子、电子邮件和多媒体文件。虽然这种类型的数据可能包含有价值的见解，但由于缺乏必要的组织，它往往不是可处理数据。然而，自然语言处理和机器学习的进步正在逐渐使从非结构化数据中提取有意义的信息成为可能，从而扩大了可以归类为可处理数据的范围。可处理数据的重要性不容低估，尤其是在决策过程中。利用可处理数据的企业可以迅速适应市场变化，更深刻地理解客户需求，并优化其运营。例如，一家零售公司通过分析销售数据，可以识别趋势并相应调整库存，确保热门产品始终有货。这种响应能力在当今快速变化的环境中至关重要，因为消费者偏好可能会迅速转变。此外，可处理数据在科学研究中也发挥着关键作用。研究人员依赖于可以轻松分析的数据来得出结论和验证假设。在医学等领域，获取可处理数据意味着科学家可以快速分析患者记录或临床试验结果，从而加快发现和创新的速度。高效操纵这些数据的能力可以显著影响新疗法和治疗方法的开发速度。总之，可处理数据的概念在我们数据中心的社会中是基础性的。它的特征，包括结构和适应性，使其成为企业、研究人员和决策者不可或缺的资源。随着技术的不断发展，构成可处理数据的定义可能会扩大，使我们能够从越来越多样化的信息源中提取见解。拥抱和理解可处理数据无疑将导致各个领域更明智的决策和更好的结果。