Recent discussions on big data applications are hot, but what is big data? Unfortunately, there is no universally accepted, standard quantitative definition. It's like a big era. I don't know what to expect to call it a big era. Amazon Web Services Expert, big data scientist John & Daw; Lauschel mentioned a simple definition: Big data is any huge amount of data that exceeds the processing power of one computer. Basically, the amount of big data has risen beyond the TB level to the PB level. To deal with such a huge amount of data, we must have corresponding, great wisdom for aggregating many people.
Why do we need big data?
First imagine a scenario where, 10 years ago and now, when you are thinking about a solution to a problem, how different are there? If you would go to a book 10 years ago, you would be asked to ask the experts after a hundred weeks, or be overwhelmed. But now, you only need to use a magical tool as the core to achieve the purpose of the network and the data behind it.
This shows that we have entered an era in which there is absolutely no lack of technology, no shortage of experts, and many are good at research and discovery. The creation of the Internet has allowed countless professionals and amateurs to actively disseminate their knowledge and technology, while also leaving many of their own behavioral information. The information is scattered. If we imagine that we can combine this information, aren't they the DNA and the blood of every technology, every individual, every thing? If we master DNA, can we not master this person/technology/thing? This idea was put in the past and we couldn't achieve it in practice because information was generated every day and could not be collected. In addition, we did not have so many scientists to process and analyze the data. However, when we entered the fourth stage of the scientific development of the data era, this idea relied on the further transformation of science and technology and will be realized.
The fourth phase of the argument was put forward by database expert James & Dot Grey, who had won the Turing Award for the Nobel Prize in computer science. In his personal biography, he wrote: Scientific development has passed the three stages of 'experiment, theory, and calculation' and entered the fourth stage of 'data'. Over the past few decades, computational science has become a popular era. Various important database technologies and algorithms have gradually matured in the past few decades; now they are due to instrument-ed and interconnected (inter-connected) worldwide. The relationship between, so that the world's data in all areas are accumulating at a very rapid pace, and the cumulative speed is far more than all enterprises can now handle the speed. Because the amount and speed of data accumulation are unprecedented, and indeed contain valuable information gold mines, in scientific research or other fields, we turn to data analysis for scientific research or corporate organization. Provide direction for development and seek breakthroughs. A comprehensive analysis of the data gives us a complete picture of what we want to know.
Of course, the idea of ​​knowing someone or technology or things through data does not drive Microsoft, IBM, Goldman Sachs, etc., global leaders of business-oriented companies to shake it off. What they are really interested in is the ability to predict the future by relying on cloud computing computers in the era of big data by analyzing the past and present characteristics of people/technology/things. Google, for example, successfully predicts four Oscars based on user data analysis. Only data from Baidu, Google, fund companies, Twitter, etc. that have a single data channel source (that is, only the company's) have achieved shocking our predictions, then when we strive to achieve the integration of all types of data, To the computer, he will bring much power to the small universe! Well, regarding the national human resources deployment, trends in business activities, and prediction of natural disasters... Although all issues cannot be fully grasped within a short period of time, it can be ensured that he will be able to optimize all existing resources and help solve everything we want to solve. The problem. What is hidden behind it is the possibility of infinity, whether political, cultural or commercial. This is the reason why all companies are calling for it. The data represents confidence and gives us confidence in understanding but not understanding things.
How can we use big data?
Although the prospects for big data applications are tempting, they can't be done without trying to do it. First of all, we need to collect a huge amount of data (acquisition technology), then consider where (storage technology), how to put it (distribution and architecture technology), how to get the computer to deal with (natural language conversion technology and data (processing technology), how to classify (statistical analysis), and then find out if the data is enough for prediction, whether it needs more data or conversion data (data mining technology), and then see if it can become predictable or The controlled model (model prediction technology) is finally understood and used by people (resulting in presentation techniques such as graphs and cloud computing). Even if any of the above eight links is difficult to complete with one's power. In the face of huge data, the wisdom of individuals is so small that it is as if ants are to understand what it means to be "the number of sands on the Ganges." This requires a huge convergence of wisdom. It requires advanced technology, talents, and a high degree of overall planning. It is not easy to do this. In simple terms, we humans are only standing at the entrance to the “fourth phase†as described above. .
It is gratifying that we also saw many examples of successful applications at this initial stage. In the power industry energy system that we are familiar with, the smart grid has now reached the end of Europe, the so-called smart meter. In Germany, in order to encourage the use of solar energy, solar energy will be installed in the home, in addition to selling electricity to you, when your solar power has excess electricity, you can buy it back. Collecting data every five minutes or ten minutes through the power grid, the collected data can be used to predict the customer's electricity usage habits, etc., and infer that the entire grid will probably need much electricity in the next 2-3 months. With this prediction, it is possible to purchase a certain amount of electricity from a power generation or power supply company. Because electricity is a bit like futures, it would be cheaper if you bought it in advance, and it would be more expensive to buy it. After this forecast, it can reduce procurement costs. The Vestas Wind System, which relies on BigInsights software and IBM supercomputers, then analyzes weather data to find the best locations for installing wind turbines and the entire wind farm. Using big data, it took weeks of analytical work and now takes less than an hour to complete.
In the communications industry, NTTdocomo combines mobile phone location information with information on the Internet to provide customers with information on nearby dining outlets, and provides last-minute information services when approaching the last bus stop. Of course, there are many apps in the country with enough data to do this.
In addition, big data applications currently have relatively frequent applications in several industries such as retailing and internet services. However, relative to its boom, it is still in its infancy. However, this trend is irreversible. Big data opens up new doors for imagination and understanding for humanity and will never stop.
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