固态硬盘损坏数据如何挽救

In my previous, articles I have mentioned about applications of big data, also there are many other different exciting applications that are being enabled by big data era.

在我之前的文章中，我提到了有关大数据应用程序的文章，还有大数据时代正在启用的许多其他激动人心的应用程序。

Our main aim while working with big data is to build methodologies and tools to make big data useful to dynamic data-driven scientific applications. Big data can be implemented in several fields such as areas of science and engineering, including genomics, geoinformatics, metro science, energy management, biomedicine, and personalized health.

在处理大数据时，我们的主要目标是建立方法和工具，使大数据对动态数据驱动的科学应用有用。 大数据可以在科学和工程领域的多个领域中实施，包括基因组学，地理信息学，地铁科学，能源管理，生物医学和个性化健康。

Let's discuss some real-life situation where big data is applied and can help us -

让我们讨论一些实际情况，其中应用了大数据并可以帮助我们-

大数据挽救生命 (Saving lives with Big Data)

We come across many firestorms daily all over the world. We cannot control such firestorms by using big data, but something we can do is to get ahead of them by predicting their behavior. This is why disaster management of ongoing wildfires heavily relies on their direction and rate of spread.

我们每天都在世界各地遇到许多风暴。 我们无法通过使用大数据来控制此类风暴，但是我们可以做的就是通过预测其行为来领先于它们。 因此，对持续不断的野火进行灾害管理时，很大程度上取决于其方向和扩散速度。

As these fires are part of our daily life we can save a lot of lives by applying Big Data.

由于这些火灾是我们日常生活的一部分，因此我们可以通过应用大数据来挽救很多生命。

大数据可以在这方面帮助我们吗？ (How big data can help us in this?)

A lot of data can be collected from sensors and satellite or other things that can help us to measure environmental factors.

可以从传感器，卫星或其他可以收集到的数据中收集很多数据，这些数据可以帮助我们测量环境因素。

Some data come from organizational data, including area maps, better service updates and filed content databases, which archive how much registers vegetation and other types of fuel are in the way of a potential fire.

一些数据来自组织数据，包括区域地图，更好的服务更新和归档的内容数据库，这些数据库归档了植被和其他类型燃料在潜在火灾中的存量。

是什么使这成为大数据问题？ (What makes this a big data problem?)

As I discussed in my previous articles also taking out value from big data is our main objective. This is the main aim behind all the activities we are doing with big data. If we cannot take out the value from the data and use it then, it is of no use.

正如我在之前的文章中讨论的那样，从大数据中获取价值也是我们的主要目标。 这是我们对大数据进行的所有活动的主要目标。 如果我们无法从数据中取出值并使用它，那就没有用了。

So, here in this case also we can predict good responses only if we can integrate this many diverse data stream. This kind of many sources existed for quite some time.

因此，在这种情况下，也只有在我们可以集成许多不同的数据流的情况下，我们才能预测良好的响应。 这种多种来源存在了相当长的时间。

But what is lacking in disaster management today is a dynamic system integration of real-time sensor, networks, satellite imagery, near real-time data management tools, connectivity to the emergency command center and all these before during and after a firestorm.

但是，当今灾难管理中缺少的是动态系统集成，包括实时传感器，网络，卫星图像，近实时数据管理工具，与紧急指挥中心的连接以及在暴风雨之前和之后的所有这些。

Most important data sources are sensor data streaming in from weather stations and satellites, such sensed data include image data humidity, air pressure, and we can also include image data streaming from mountaintop cameras and satellites in this category.

最重要的数据源是来自气象站和卫星的传感器数据流，这些感测的数据包括图像数据湿度，气压，并且我们还可以包括来自此类山顶摄像机和卫星的图像数据流。

A huge part of data is also generated by the public on social media sites, such as facebook, twitter etc. These people generated data are the hardest to streamline during an existing fire, but they can be very valuable once integrated with other data sources. Imagine synthesizing all the pictures on twitter about us ongoing fire or checking the public sentiment around the boundaries of a fire.

公众也在社交媒体网站(例如facebook，twitter等)上生成大量数据。这些人生成的数据在现有火灾中最难精简，但一旦与其他数据源集成在一起，它们将非常有价值。 想象一下，将推特上关于我们正在进行的大火的所有图片合成在一起，或者检查围绕大火边界的公众情绪。

Once we have access to such information at our fingertips there are many things we can do with such data.

一旦我们可以触及这些信息，便可以使用这些数据做很多事情。

Let's take a 2^nd example, as by analyzing a person’s genetics, his/her environment, daily activities we can detect or predict a health problem early, help prevent disease and in case of illness provide the right drug at the right dose that is suitable just for that particular person. But the main thing is to collect, store, integrate, collaborate and most important to extract value from these data.

让我们举^第二个例子，因为通过分析一个人的遗传学，他/她的环境，日常活动，我们可以及早发现或预测健康问题，帮助预防疾病，并且在生病的情况下以合适的剂量提供合适的药物仅针对特定的人。 但是最主要的是收集，存储，集成，协作，而最重要的是从这些数据中提取价值。

Conclusion:

结论：

In this article, I discussed several applications of Big data by which we can save a number of lives. We will discuss many other applications of Big data in real life in my upcoming articles. For any queries shoot your questions in the comment section below. See, you in my next article till then stay healthy and keep learning.

在本文中，我讨论了大数据的几种应用，通过这些应用我们可以挽救许多生命 。 在我即将发表的文章中，我们将讨论现实中大数据的许多其他应用。 如有任何疑问，请在下面的评论部分中提出您的问题。 在我的下一篇文章中，您可以看到，直到您保持健康并继续学习。

翻译自: https://www.includehelp.com/big-data/saving-lives-with-big-data.aspx

固态硬盘损坏数据如何挽救