Scientific and technological developments in recent years, with the rapid development in the field of intelligent home, car and other things, things have been increasingly permeate all aspects of people's lives. Simply put, the Internet of Things is the use of communication technologies such as local networks or the Internet to connect sensors, controllers, machines, people and things in a new way to form people, things, things and things, and to realize informationization. Remote management control and intelligent network.
The internet of things connected to everything will generate very valuable big data. These data can help cities predict accidents and crimes; allow doctors to view information in patients' pacemakers and biochips in real time; optimize predictive maintenance equipment and machinery to optimize industrial output; connect with home appliances It provides users with crisis communication with their own vehicles and handheld devices to build truly smart homes. These possibilities are brought about by the Internet of Things, and it will bring more and more convenience.
As the devices and sensors connected to the Internet of Things continue to expand rapidly, the amount of data generated by these devices will grow to an incredible level. This data has great value, allowing us to see what is useful and what is useless.
This sounds good. However, the biggest problem will be finding a way to analyze the enormous amount of data and information generated by these devices. If you have ever tried to find out what files you need in a few terabytes of data, then you will know how difficult it is. For humans, it is impossible to review and understand all of these data. If you do this with traditional methods, even if you reduce the sample size, it will take a lot of time.
We need to improve the speed and accuracy of big data analytics so that the Internet of Things can do what it promises. If we do not do this, the consequences will be catastrophic, ranging from small to household appliances can not communicate with each other, to life-threatening - cardiac pacemaker failure or hundreds of cars in a chain collision.
The only way to keep up with the speed of data generated by the Internet of Things and to mine hidden data in the data is to use machine learning capabilities.
Machine learning has been applied <br> <br> Wikipedia machine learning is defined as "Computer Science (CS) and artificial intelligence (AI) sub-domains, with the construction and research systems, we can learn from the data itself, and It's not just following explicit program instructions."
Although this sounds a bit like science fiction, it already exists in everyday life. For example, Pandora, a streaming music player, is using this technology to determine which other songs users will like; Amazon.com will also provide users with recommendations for other books and movies. Both of the above are through the monitoring of user behavior, the system automatically learns and makes judgments.
In the case of the Internet of Things, machine learning can help companies attribute the billions of data points they have to truly meaningful information. The general premise of machine learning is the same. Review and analyze the data you collect and learn the “information†from the data so that we can make better decisions.
For example, keeping track of your healthy wearable devices has become a new industry, but these devices will soon become products that can connect to each other and to the Internet, track your health and provide real-time updated health services.
What the IoT needs to do is that your doctor will receive notifications when your physical health is up. For example, your heart rate has risen to an unsafe level, and even stopped. In order to find potential problems, data analysis needs to know what is normal and what is not. Similarities, correlations, and anomalies need to be quickly identified based on real-time data flow.
For immediate data analysis, it can accurately identify previously known information and new information that has not been seen before. Machines have the ability to generate and aggregate these big data. These big data must also be used to learn the normal behavior of each patient and track, find and mark everything outside of normal behavior, because these may indicate that the patient has serious health problems. .
The realization of the Internet of Things depends on being able to obtain information hidden in the existing huge data and ever-growing data, and then make in-depth information mining. The realization of the Internet of Things in the future depends on machine learning and will improve every aspect of our daily life.
The internet of things connected to everything will generate very valuable big data. These data can help cities predict accidents and crimes; allow doctors to view information in patients' pacemakers and biochips in real time; optimize predictive maintenance equipment and machinery to optimize industrial output; connect with home appliances It provides users with crisis communication with their own vehicles and handheld devices to build truly smart homes. These possibilities are brought about by the Internet of Things, and it will bring more and more convenience.
As the devices and sensors connected to the Internet of Things continue to expand rapidly, the amount of data generated by these devices will grow to an incredible level. This data has great value, allowing us to see what is useful and what is useless.
This sounds good. However, the biggest problem will be finding a way to analyze the enormous amount of data and information generated by these devices. If you have ever tried to find out what files you need in a few terabytes of data, then you will know how difficult it is. For humans, it is impossible to review and understand all of these data. If you do this with traditional methods, even if you reduce the sample size, it will take a lot of time.
We need to improve the speed and accuracy of big data analytics so that the Internet of Things can do what it promises. If we do not do this, the consequences will be catastrophic, ranging from small to household appliances can not communicate with each other, to life-threatening - cardiac pacemaker failure or hundreds of cars in a chain collision.
The only way to keep up with the speed of data generated by the Internet of Things and to mine hidden data in the data is to use machine learning capabilities.
Machine learning has been applied <br> <br> Wikipedia machine learning is defined as "Computer Science (CS) and artificial intelligence (AI) sub-domains, with the construction and research systems, we can learn from the data itself, and It's not just following explicit program instructions."
Although this sounds a bit like science fiction, it already exists in everyday life. For example, Pandora, a streaming music player, is using this technology to determine which other songs users will like; Amazon.com will also provide users with recommendations for other books and movies. Both of the above are through the monitoring of user behavior, the system automatically learns and makes judgments.
In the case of the Internet of Things, machine learning can help companies attribute the billions of data points they have to truly meaningful information. The general premise of machine learning is the same. Review and analyze the data you collect and learn the “information†from the data so that we can make better decisions.
For example, keeping track of your healthy wearable devices has become a new industry, but these devices will soon become products that can connect to each other and to the Internet, track your health and provide real-time updated health services.
What the IoT needs to do is that your doctor will receive notifications when your physical health is up. For example, your heart rate has risen to an unsafe level, and even stopped. In order to find potential problems, data analysis needs to know what is normal and what is not. Similarities, correlations, and anomalies need to be quickly identified based on real-time data flow.
For immediate data analysis, it can accurately identify previously known information and new information that has not been seen before. Machines have the ability to generate and aggregate these big data. These big data must also be used to learn the normal behavior of each patient and track, find and mark everything outside of normal behavior, because these may indicate that the patient has serious health problems. .
The realization of the Internet of Things depends on being able to obtain information hidden in the existing huge data and ever-growing data, and then make in-depth information mining. The realization of the Internet of Things in the future depends on machine learning and will improve every aspect of our daily life.
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