Today is the information age, the data is the best in the world. However, just “having†data is not enough, the “accuracy†of the data and the “analysis†of the data are also crucial. Einstein also said: "It is not necessarily useful to use, and useful is not always useful."
“Data†and “information†are not the same thing. “Data†refers to a bunch of unprocessed raw measurements, we analyze it and take its essence to its dross for useful information. Therefore, we often say that "information overload" is actually wrong, "data" may be overloaded, but the more "information" the better. The data itself is not necessarily useful, because without proper screening, the data may be like false news, causing us to go astray.
Over the past decade, our data volume has exploded. The New York Times reported that the total amount of global data reached 130 billion GB in 2005. Today's companies often have to process data in PB. With the rapid growth of data sources, data acquisition speed is getting faster and faster. The progress of science and technology is so rapid, and it is already a slap in the eye. According to @HistoricalPics Twitter, in 1956, a 5MB hard drive would have to weigh more than 2,000 pounds. IBM had to use a plane to transport it! Looking down at the big mobile phone, I couldn’t help feeling.
With the measurement of people's activities and sensors, the type of data is also increasing. And we have to remember: data, only after analysis, becomes information is useful.
The advantage of the Internet of Things is that it can acquire and organize data in real time. If the architecture is correct, the Internet of Things can turn data into useful information to determine what to do next.
KrisTIan J. Hammond said in the Harvard Business Review: "Most of the time, we all know what we want from the data: we know what we need to analyze, what we need to look for, how we need to compare. We You can pass the data to a machine that can do the job, and then let it tell us the results in a human way and in natural language. This way, we can extract a lot of useful information from the data in a stable and rapid way – but now Not realized. By supplementing the power of the machine, we can fully automate the gold mining from the data, making the cold figures into perceptual cognition."
How to discover the meaning of the data?
Before the Internet of Things, it was very difficult to analyze the vast amounts of data of sensors. Through the Internet of Things technology, we can put the data obtained by the machine into the data pool for automatic analysis to determine what needs to be done for the data and the program in the next step. The Internet of Things not only collects and analyzes data, it also promotes itself.
Before we introduce the specific steps, let's clarify two terms that are commonly used when discussing data transmission: "northbound" and "southbound". “Northbound data†refers to data sent from the device and sent to the cloud through the gateway, usually telemetry data, or command and control requests. “Southward data†is sent from the cloud to the gateway, or from the cloud to the device through the gateway, usually command and control information (such as software updates, requests, configuration parameters, etc.).
Here's how to find useful information from the probe data using the South and Northbound channels:
Step 1: The sensor sends northbound telemetry data. Depending on the architecture, this data is pre-processed and sent to a data store (such as a gateway) located near the sensor.
Step 2: Perform a certain amount of analysis on the temporary node of the gateway, where you can process the data (such as summarizing data, or transforming the data to prepare for data center or cloud in-depth analysis). Then, the information processed on the gateway is compared with the previous accurate result, that is, the correlation matching is performed in the history information. The patterns found can be used as the basis for our actions. But in addition to discovering known patterns, you also want to find things you don't know and want to discover new correlations and conclusions. For example, you may not know that when the temperature drops below 10 °C, the anti-flu prescription prescribed by doctors will increase by 30%, while the sales of chicken soup and paper towels will increase within 10 days. You may not have noticed these connections before, but now with the Internet of Things, you can use these to make new business decisions.
Step 3: With the new information, you can create a rule. For example, when the sensor finds that the temperature has dropped below 10 °C, let the warehouse transport the chicken soup and paper towels near the dock. In this way, you turn information into rules of action that can be monitored, managed, and executed.
Step 4: Finally put the well-established rules into practice. It is the iterative process as shown.
Open source He Yi?
Open source software projects provide standardized toolkits (such as Camel, Drools) that you can use to process and manipulate data. Apache Camel is a Java-based routing and mediation engine with an enterprise integration model that can process data. It can be used for network solution development through out-of-the-box information mediation, routing, and data transformation. I think it's best to use Apache Camel in IoT through the Eclipse IoT workgroup project (like Eclipse Kapua, Kura).
Drools in the JBoss community is a business rules management system with built-in rule templates that you can use to specify when to take action. Drools implements the rules required by the Internet of Things and the scalability required by the optimization rules engine through well-defined DSLs (domain-specific languages). It also comes with a GUI called Workbench that allows developers to create and edit rules very simply.
Turning data into useful information is at the heart of all IoT efforts, and this can be achieved with open source software, which helps accelerate the adoption of the Internet of Things.
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