Tag: automation”

What is Data Mining and Data Science? Thorough explanation of differences and outline

In recent years, “big data” has attracted a great deal of attention, and how to utilize big data in corporate activities has become an urgent issue in all industries. Therefore, in this article, we will focus on “data mining” and “data science” related to the handling of data.

About data mining and data science

First, let’s take a look at the definitions and differences between data mining and data science.
What is data mining?
Data mining is a technique for finding “knowledge” in a large amount of data by making full use of analysis methods such as statistics and AI. As the word data mining implies, it means mining useful information (data).
What is data science?
Data science is a research field for extracting meaningful data using methods in various fields such as statistics and information engineering. Data science is a collection of many research fields, and has received more attention in recent years due to the growing social needs.
Differences between data mining and data science
Data science is required to carry out all processes from data acquisition, accumulation, analysis, model construction, verification, and problem solving. Data mining, on the other hand, is primarily focused on analysis and model building within this step.

The main methods of data mining

Many of the methods used in data mining are those used in statistical analysis and are considered to be useful in data mining as well. From here, I will explain the typical methods of data mining.
Market basket
A market basket is a technique used to discover items that are often bought at the same time from retail store sales data. By visualizing products that seem to have little relevance, such as baby diapers and canned beer, but are often purchased at the same time, it helps to create an effective sales floor.
Clustering
Clustering is a method of grouping people who have similar behaviors from purchasing data and taking appropriate measures for each group. Classification based on data similarity makes it easier to launch different marketing for each group.
Logistic regression analysis
Logistic regression analysis is a statistical method that can explain and predict the probability that a value result (objective variable) will occur from several factors (explanatory variables). Since it is an analysis method that determines the “occurrence rate of a certain event,” it can be expected to be used in various business situations.
Machine learning
In some cases, data mining uses machine learning that utilizes AI. Programming languages such as “Python” and “R” are often used for data analysis by machine learning. In particular, Python has a wealth of libraries that are useful for data analysis, making it an effective language for discovering knowledge that finds rules and relationships from data.

Data mining implementation procedure

When performing data mining, it is important to take the right steps. The following describes the specific steps required to perform data mining.
Collect data
First, collect the data that suits your purpose. By collecting as much data as possible, it will be easier to find useful data.
Process and organize data
Next, we will process and organize the collected data into a form suitable for learning. If there is a lot of useless information called “noise” or irrelevant information, AI will not be able to learn correctly. Therefore, when organizing your data, you should remove noise and analyze using only the information you need.
Analyze the data
After processing and organizing the data, we will discover and group the patterns of the data using the methods such as clustering, logistic regression analysis, and market basket introduced above.
Conduct verification / evaluation
You may find some rules or relationships in the patterns and groups derived from the analysis. In such cases, apply the discovered rules and relationships to other data, verify and evaluate whether it can be said as a general theory or as a tendency.

Example of data science utilization
So how is data science actually used in the business scene? Below, we will introduce specific use cases of data science.
Retail business
In the retail industry, leveraging a customer database can help you run more effective campaigns and make effective offers to your customers. For example, linking purchase-related data such as “when”, “who”, “where”, “what you purchased”, “what other products you were interested in”, market data, customer data, etc. By aggregating, it is possible to clarify customer behavior patterns and preferences. On top of that, if you narrow down the targets that are likely to be purchased, you can come up with effective marketing measures such as coupon distribution according to customer preferences.
It is also possible to predict future trends by combining SNS posts and Web behavior data. As a result, product demand can be predicted accurately, the number of inventories to be secured can be grasped, and inventory control can be performed, which can be expected to increase sales and reduce inventory loss at the same time.
Financial industry
In the financial industry, stock price and foreign exchange forecasts can be made by combining past stock transaction data and foreign exchange data with various economic indicators occurring in the world.
Nowadays, AI predicts not only the selection of stocks but also the timing of buying and selling, and services for automatically purchasing foreign currencies have begun to emerge, and such services are expected to become more widespread in the future.
Restaurant business
In recent years, the use of data science has been promoted in the restaurant industry as well. In fact, many stores have adopted electronic payments and loyalty points cards, and it has become possible to analyze purchasing behavior and store visit history for each customer.
In addition, when sales are not expected, we can reduce costs such as food loss by optimizing ingredients and personnel. One of the merits of utilizing data science is that it becomes easier for the restaurant industry to think about measures according to sales forecasts in advance.

Skills useful for data science
Data scientists are required to solve corporate management issues by collecting and utilizing data. To achieve this, three skills, “statistical analysis skills,” “language skills,” and “IT skills,” are indispensable. Here, we will explain why each skill is necessary.
Statistical analysis skills
Data scientists are specialists in the handling and analysis of big data. Therefore, skills to analyze statistics based on the derived data are required. Be sure to acquire mathematical knowledge such as probability, statistics, calculus, and matrix.
Language skill
In the business scene, it is required to explain the analysis results in an easy-to-understand and smooth manner even for people without specialized knowledge. In particular, in recent years, the employment of foreign workers in Japan has been increasing year by year due to the effects of the declining birthrate and aging population. It can be said that a certain level of language proficiency is an indispensable skill for smooth communication with business partners and employees.
IT skills
Data scientists who handle data naturally need general knowledge of IT. “Database knowledge”, “skills for high-speed data processing”, “programming skills”, etc. are indispensable skills for carrying out business, so it is recommended to learn repeatedly.

UMWELT of TRYETING that can effectively utilize big data!
If you want to make effective use of big data accumulated in-house, why not use TRYETING’s no-code AI cloud “UMWELT”. Since it is equipped with many algorithms that are useful for data analysis, you can easily build an AI system with just a mouse operation. Another strength of UMWELT is that the period until the introduction of AI is 1/4 of the conventional one, which enables high-speed introduction, and the introduction cost is 1/10 of the conventional one, which is the lowest cost in the industry.
Summary
This time, we introduced the differences and outlines between data mining and data science, as well as specific application examples. In the modern society where the environment and methods for handling big data have developed, the technology to obtain knowledge from data is an extremely powerful weapon. By all means, please refer to this article to firmly control the data mining process and improve the prediction accuracy.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy
July 1, 2024
What is Data Mining and Data Science? Thorough explanation of differences and outline

In recent years, “big data” has attracted a great deal of attention, and how to utilize big data in corporate activities has become an urgent issue in all industries. Therefore, in this article, we will focus on “data mining” and “data science” related to the handling of data.

About data mining and data science

First, let’s take a look at the definitions and differences between data mining and data science.
What is data mining?
Data mining is a technique for finding “knowledge” in a large amount of data by making full use of analysis methods such as statistics and AI. As the word data mining implies, it means mining useful information (data).
What is data science?
Data science is a research field for extracting meaningful data using methods in various fields such as statistics and information engineering. Data science is a collection of many research fields, and has received more attention in recent years due to the growing social needs.
Differences between data mining and data science
Data science is required to carry out all processes from data acquisition, accumulation, analysis, model construction, verification, and problem solving. Data mining, on the other hand, is primarily focused on analysis and model building within this step.

The main methods of data mining

Many of the methods used in data mining are those used in statistical analysis and are considered to be useful in data mining as well. From here, I will explain the typical methods of data mining.
Market basket
A market basket is a technique used to discover items that are often bought at the same time from retail store sales data. By visualizing products that seem to have little relevance, such as baby diapers and canned beer, but are often purchased at the same time, it helps to create an effective sales floor.
Clustering
Clustering is a method of grouping people who have similar behaviors from purchasing data and taking appropriate measures for each group. Classification based on data similarity makes it easier to launch different marketing for each group.
Logistic regression analysis
Logistic regression analysis is a statistical method that can explain and predict the probability that a value result (objective variable) will occur from several factors (explanatory variables). Since it is an analysis method that determines the “occurrence rate of a certain event,” it can be expected to be used in various business situations.
Machine learning
In some cases, data mining uses machine learning that utilizes AI. Programming languages such as “Python” and “R” are often used for data analysis by machine learning. In particular, Python has a wealth of libraries that are useful for data analysis, making it an effective language for discovering knowledge that finds rules and relationships from data.

Data mining implementation procedure

When performing data mining, it is important to take the right steps. The following describes the specific steps required to perform data mining.
Collect data
First, collect the data that suits your purpose. By collecting as much data as possible, it will be easier to find useful data.
Process and organize data
Next, we will process and organize the collected data into a form suitable for learning. If there is a lot of useless information called “noise” or irrelevant information, AI will not be able to learn correctly. Therefore, when organizing your data, you should remove noise and analyze using only the information you need.
Analyze the data
After processing and organizing the data, we will discover and group the patterns of the data using the methods such as clustering, logistic regression analysis, and market basket introduced above.
Conduct verification / evaluation
You may find some rules or relationships in the patterns and groups derived from the analysis. In such cases, apply the discovered rules and relationships to other data, verify and evaluate whether it can be said as a general theory or as a tendency.

Example of data science utilization
So how is data science actually used in the business scene? Below, we will introduce specific use cases of data science.
Retail business
In the retail industry, leveraging a customer database can help you run more effective campaigns and make effective offers to your customers. For example, linking purchase-related data such as “when”, “who”, “where”, “what you purchased”, “what other products you were interested in”, market data, customer data, etc. By aggregating, it is possible to clarify customer behavior patterns and preferences. On top of that, if you narrow down the targets that are likely to be purchased, you can come up with effective marketing measures such as coupon distribution according to customer preferences.
It is also possible to predict future trends by combining SNS posts and Web behavior data. As a result, product demand can be predicted accurately, the number of inventories to be secured can be grasped, and inventory control can be performed, which can be expected to increase sales and reduce inventory loss at the same time.
Financial industry
In the financial industry, stock price and foreign exchange forecasts can be made by combining past stock transaction data and foreign exchange data with various economic indicators occurring in the world.
Nowadays, AI predicts not only the selection of stocks but also the timing of buying and selling, and services for automatically purchasing foreign currencies have begun to emerge, and such services are expected to become more widespread in the future.
Restaurant business
In recent years, the use of data science has been promoted in the restaurant industry as well. In fact, many stores have adopted electronic payments and loyalty points cards, and it has become possible to analyze purchasing behavior and store visit history for each customer.
In addition, when sales are not expected, we can reduce costs such as food loss by optimizing ingredients and personnel. One of the merits of utilizing data science is that it becomes easier for the restaurant industry to think about measures according to sales forecasts in advance.

Skills useful for data science
Data scientists are required to solve corporate management issues by collecting and utilizing data. To achieve this, three skills, “statistical analysis skills,” “language skills,” and “IT skills,” are indispensable. Here, we will explain why each skill is necessary.
Statistical analysis skills
Data scientists are specialists in the handling and analysis of big data. Therefore, skills to analyze statistics based on the derived data are required. Be sure to acquire mathematical knowledge such as probability, statistics, calculus, and matrix.
Language skill
In the business scene, it is required to explain the analysis results in an easy-to-understand and smooth manner even for people without specialized knowledge. In particular, in recent years, the employment of foreign workers in Japan has been increasing year by year due to the effects of the declining birthrate and aging population. It can be said that a certain level of language proficiency is an indispensable skill for smooth communication with business partners and employees.
IT skills
Data scientists who handle data naturally need general knowledge of IT. “Database knowledge”, “skills for high-speed data processing”, “programming skills”, etc. are indispensable skills for carrying out business, so it is recommended to learn repeatedly.

UMWELT of TRYETING that can effectively utilize big data!
If you want to make effective use of big data accumulated in-house, why not use TRYETING’s no-code AI cloud “UMWELT”. Since it is equipped with many algorithms that are useful for data analysis, you can easily build an AI system with just a mouse operation. Another strength of UMWELT is that the period until the introduction of AI is 1/4 of the conventional one, which enables high-speed introduction, and the introduction cost is 1/10 of the conventional one, which is the lowest cost in the industry.
Summary
This time, we introduced the differences and outlines between data mining and data science, as well as specific application examples. In the modern society where the environment and methods for handling big data have developed, the technology to obtain knowledge from data is an extremely powerful weapon. By all means, please refer to this article to firmly control the data mining process and improve the prediction accuracy.

Follow us on Facebook for updates and exclusive content! Click here: Each Techy
July 1, 2024
What is an IoT system? Explaining the basic configuration, development flow, examples, etc.
“I hear that IoT is popular, but I don’t know how it works.” “I don’t know how to proceed with IoT system development.”
If you are reading this article, you may have questions like the ones above. In this article, we will explain the configuration of the IoT system, what it can do, the development flow, and usage examples. We also introduce the “IoT System Technology Certification”, which is a test for those who want to learn about IoT systems, so please refer to it.

IoT and IoT systems
IoT (Internet of Things) is a technology in which devices and sensors communicate via the Internet to collect and share data. Smart home appliances that have been released for home use in recent years are also products that use IoT technology.
As the name suggests, an IoT system is a system that uses IoT technology. It is used not only in factories but also in vending machines and home appliances, and it is expected that collecting and analyzing data will make life more comfortable and make work more efficient.

IoT system components
The IoT system consists of the following elements:
- device
- network
- application
- storage
Device
Devices mainly refer to hardware such as actuators, temperature sensors, humidity sensors, cameras, and smart meters. The device’s role is to collect the following data.
- temperature
- humidity
- Whether there are humans or pets
- Power usage status
It is also possible to operate on “things”. For example, perform the following actions:
- make a sound
- Operate the remote control
- opening/closing the door
- Discharging food given to pets
Devices in IoT also have the role of connecting to the Internet and sending and receiving data.
Network
IoT systems require networks to send and receive data between devices and between devices and applications. The following are network technologies used in IoT systems.
- Wifi
- Bluetooth
- 3G, 4G, 5G
- M2M communication (device-to-device communication)
In order for a device to process the information it collects, it must send and receive data. The wireless and mobile networks mentioned above are the constituent elements of the data transmission and reception path in the IoT system.
Application
In IoT systems, applications are used to process and analyze data. It may be easier to understand if you think of them as being in charge of utilizing the collected data.
The application also has the role of issuing instructions to the device based on the analyzed information. In the case of an air conditioner IoT system, energy savings can be achieved by issuing an instruction such as “According to the data collected by the sensor, there is no one around at the moment, so let’s turn off the power.”
Storage
Storage is used as a place to store collected data. In many cases, cloud storage will be used. In IoT systems, the amount of data collected by devices increases as the usage time increases.
With more data, applications are likely to perform more effective analysis. However, there is no numerical value such as “the optimal amount of data is XX GB”, nor does it mean that more data is better. Therefore, data needs to be collected and stored for a while. By using storage with a large upper limit that can be collected, it is possible to accumulate and analyze the necessary data.
Background to the spread of IoT systems
It can be said that IoT systems have spread rapidly in recent years.
- Network acceleration
- Popularization of smaller devices
- Reducing manufacturing costs through the development of advanced technology
Network acceleration
One of the important factors behind the spread of IoT systems is the increase in network speed. Recently, high-speed and stable Internet connections have become widespread, and mobile communication technology continues to develop. These advances in network technology have enabled large numbers of devices to send and receive data simultaneously. By using a high-speed network, we are able to exchange information in real time with little communication delay.
As mentioned earlier, networks are essential for IoT systems. As network technology has evolved and data transmission and reception within IoT systems has become faster, convenience has improved. As a result, it is thought that the number of people who want to use IoT systems continues to increase.
Popularization of smaller devices
One of the reasons behind the spread of IoT systems is the spread of small and power-saving devices. Among the components of IoT systems, this is the development of devices.
Advances in industrial technology and science have made it possible to create small yet high-performance devices such as sensors and controllers. Small devices are more energy efficient than large devices, and have the advantage of being easier to use in household products. Advances in device miniaturization have made it possible to incorporate devices into a variety of products.
Even small devices can collect data in real time. It can be said that the progress made in achieving smaller size and higher performance of IoT devices is directly linked to the spread of IoT systems.
Manufacturing cost reduction through the development of advanced technology
The reason behind the spread of IoT systems is the decline in manufacturing costs due to the development of advanced technology. This is also a story about devices among the components of an IoT system. IoT devices have also become cheaper to manufacture due to advances in semiconductor technology, microprocessors, energy efficient device designs, and many other related technologies.
Furthermore, as an advanced technology, the application aspects of the components of IoT systems are also evolving. Advances in cloud computing and big data technology have also reduced the cost of data collection and processing.
The above two technologies have made it possible to manufacture high-performance IoT devices at low cost, and the convenience of the IoT system as a whole has improved, which is why IoT systems are becoming more popular.
What you can do with IoT systems
The following are examples of what can be done with IoT systems.
- Operations on things
- Monitoring the status and operation of things
- Communication between things
Operations on things
IoT systems allow users (humans) to operate things remotely.
For example, in a smart house, you can use your smartphone or tablet to adjust the temperature of your home’s air conditioner, turn on/off lights, and feed your pet. Smart cars also use mobile apps to unlock the car’s doors and start the engine.
By using IoT systems, it is possible to operate things via a network even when the user is not present.
Monitoring the status and operation of things
IoT systems can monitor the status and operation of things in real time. Devices such as sensors and actuators are embedded in products to collect and send data to applications and storage.
For example, a refrigerator with a built-in temperature sensor monitors the temperature inside the refrigerator and notifies the user if the temperature is abnormal. This not only allows users to notice abnormalities inside the refrigerator, but also helps prevent food from deteriorating if noticed early.
Let’s also consider the example of a smart meter installed as an electricity meter. Smart meters not only monitor electricity usage and notify the utility company, but also provide recommendations to users on how to improve their electricity usage based on the collected data and analysis results.
By monitoring the status and operation of things through IoT systems, it is possible to improve efficiency and safety, and discover problems early.
Communication between things
IoT systems allow things (products) to communicate with each other.
For example, in a smart home, sensors and smartphones communicate to share data and perform operations. In addition, in factories, manufacturing lines that use IoT systems allow machines to share data with each other. As a result, it will be possible to understand the details and progress of the preceding and succeeding processes, which will help improve operational efficiency and detect abnormalities.
The use of IoT systems allows things to communicate with each other, making life and work more efficient and convenient.

IoT system development flow
The general waterfall development flow for IoT systems is as follows.
- Requirements definition and design
- implementation
- quality test
- Release and maintenance operations
Requirements definition and design
Define requirements at the beginning of IoT system development. This is the process of clarifying the purpose and functionality of the system, working backwards from what users want, and understanding requirements and business needs.
Requirements definition clarifies what devices and sensors are needed, how data will be acquired and processed, network connectivity, security requirements, etc. In addition, the necessary budget, number of development team members, period, etc. are determined at the requirements definition stage.
Design is done after requirements definition. We provide technical details for the content documented in the requirements definition. Specific details include device architecture, data flow (network), user interface, etc.
Implementation
Once requirements definition and design are complete, implementation begins. We will actually create a pre-designed system as a program. The specific content to be implemented in an IoT system includes configuring devices and sensors, building network connections, and processing data. If you have any content that has been designed, such as cooperation with applications or cloud services, please implement it for those as well.
During implementation, we select an appropriate programming language and framework in advance, and optimize the programming itself from the standpoint of safety and performance.
Quality test
After the implementation is complete, we will conduct a quality test. Specific examples of quality tests include functional tests, performance tests, unit tests, and coordination tests.
Functional test: Check whether it can operate as required
Performance test: Evaluate whether it can withstand a certain load, communication speed, processing speed, etc.
Unit test: Check whether one function operates as expected
Linkage testing: Not only does it meet the specifications, but it also confirms that there are no abnormalities in areas where there were no problems in the unit test when multiple functions are combined.
If we determine that the quality does not meet the standards through quality testing, we will provide feedback to the design and implementation.
Release and maintenance operations
After passing the quality test, release your IoT system. At release, we will install and configure the system and create documentation for users.
After release, system maintenance and operation will begin. The goals of maintenance operations are to maintain system stability and performance, and improve user satisfaction. Specifically, we carry out periodic bug fixes and troubleshooting for the entire system.
It is also necessary to improve customer support. You may end up developing new features based on actual customer feedback.

Examples of IoT system introduction
We will introduce three examples of introducing IoT systems.
- Apartment that looks after seniors
- You can check the status of the trash can
Apartment that looks after seniors
Families who live far away from an elderly person often find it burdensome to visit them regularly to check on their condition. This condominium collects data on usage frequency and status by having customers use home appliances that incorporate an IoT system.
If an abnormality is detected in the data, a notification will be sent to the condominium management office, which will help prevent any accidents. By using the IoT system, we are contributing not only to families in need of nursing care, but also to the nursing care industry.

You can check the status of the trash can
Nippon Systemware has developed “BigBelly Solar,” a trash can that incorporates IoT. The collection status of garbage accumulated in the trash can is visualized, making garbage collection more efficient. It also has an automatic garbage compaction function, so you can check the accumulation status and have the advantage of preventing garbage from overflowing from the trash can.
By visualizing the collection status of each trash can, you will be able to see which trash cans are used more frequently and which ones are less frequently used, and you will be able to more efficiently consider areas where you should increase or decrease the number of trash cans.
This is an example that shows how using IoT devices can help solve familiar problems.
Sunstar has developed G・U・M PLAY, a device that works with a smartphone to record tooth brushing status. By incorporating a sensor into your toothbrush, you can score your tooth brushing based on the movement of your toothbrush and the time you brush.
Users can use a smartphone app to view the data collected by the toothbrush and understand how well they are brushing their teeth. The app analyzes your tooth brushing data and gives you advice on how to best brush your teeth.
This is an example of using an IoT system to visualize something that you cannot understand even if you do it yourself.
Introduction to IoT system certification
From here, we will explain the following two contents about the “IoT System Technology Certification Exam”, an exam that can check the development and knowledge of IoT systems.
- Overview of IoT System Certification and Benefits of Taking the Test
- Skills and knowledge gained through IoT system certification
Overview of IoT System Certification and Benefits of Taking the Test
The exam has three levels: advanced, intermediate, and basic. Only the advanced level requires exam qualification, and you must have passed the intermediate level or taken a certification program. The exam method is basic, with the intermediate level being a CBT exam (multiple choice questions), and the advanced level being an essay exam based on the course content and course content.
If you are a beginner in IoT systems or are taking the test for the first time, it is recommended that you take the test from the basic level. The purpose of the IoT System Certification is to develop IoT engineers, and each exam requires a certain level of knowledge.
The benefit of passing the test is that you will have more opportunities to work in businesses related to IoT system development (including sales, etc.). Having a qualification will show off that you have knowledge, which can also be used when looking for a job.

Skills and knowledge gained through IoT system certification
The skills gained through the IoT System Certification are the knowledge that is appropriate to the level of each exam. For example, at the basic level, the content would be as follows.
- IoT system configuration and construction technology
- Sensor/actuator technology and communication method
- IoT data utilization technology (AI)
- IoT information security measures technology
- IoT system prototyping technology
There are some parts that overlap with the content explained in this article. The exam will ask you about more detailed information, so it is a good idea to prepare for the exam using a question set.
summary
We provided an overview of the IoT system, its components, the background of its spread, what it can do, the development flow, implementation examples, and IoT system certification.
The number of household appliances that use IoT systems is increasing every day, and this trend is expected to continue in the future. In order to coexist with IoT systems in your life, it is important to understand the basics explained in this article.

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May 26, 2024
What is an IoT system? Explaining the basic configuration, development flow, examples, etc.
“I hear that IoT is popular, but I don’t know how it works.” “I don’t know how to proceed with IoT system development.”
If you are reading this article, you may have questions like the ones above. In this article, we will explain the configuration of the IoT system, what it can do, the development flow, and usage examples. We also introduce the “IoT System Technology Certification”, which is a test for those who want to learn about IoT systems, so please refer to it.

IoT and IoT systems
IoT (Internet of Things) is a technology in which devices and sensors communicate via the Internet to collect and share data. Smart home appliances that have been released for home use in recent years are also products that use IoT technology.
As the name suggests, an IoT system is a system that uses IoT technology. It is used not only in factories but also in vending machines and home appliances, and it is expected that collecting and analyzing data will make life more comfortable and make work more efficient.

IoT system components
The IoT system consists of the following elements:
- device
- network
- application
- storage
Device
Devices mainly refer to hardware such as actuators, temperature sensors, humidity sensors, cameras, and smart meters. The device’s role is to collect the following data.
- temperature
- humidity
- Whether there are humans or pets
- Power usage status
It is also possible to operate on “things”. For example, perform the following actions:
- make a sound
- Operate the remote control
- opening/closing the door
- Discharging food given to pets
Devices in IoT also have the role of connecting to the Internet and sending and receiving data.
Network
IoT systems require networks to send and receive data between devices and between devices and applications. The following are network technologies used in IoT systems.
- Wifi
- Bluetooth
- 3G, 4G, 5G
- M2M communication (device-to-device communication)
In order for a device to process the information it collects, it must send and receive data. The wireless and mobile networks mentioned above are the constituent elements of the data transmission and reception path in the IoT system.
Application
In IoT systems, applications are used to process and analyze data. It may be easier to understand if you think of them as being in charge of utilizing the collected data.
The application also has the role of issuing instructions to the device based on the analyzed information. In the case of an air conditioner IoT system, energy savings can be achieved by issuing an instruction such as “According to the data collected by the sensor, there is no one around at the moment, so let’s turn off the power.”
Storage
Storage is used as a place to store collected data. In many cases, cloud storage will be used. In IoT systems, the amount of data collected by devices increases as the usage time increases.
With more data, applications are likely to perform more effective analysis. However, there is no numerical value such as “the optimal amount of data is XX GB”, nor does it mean that more data is better. Therefore, data needs to be collected and stored for a while. By using storage with a large upper limit that can be collected, it is possible to accumulate and analyze the necessary data.
Background to the spread of IoT systems
It can be said that IoT systems have spread rapidly in recent years.
- Network acceleration
- Popularization of smaller devices
- Reducing manufacturing costs through the development of advanced technology
Network acceleration
One of the important factors behind the spread of IoT systems is the increase in network speed. Recently, high-speed and stable Internet connections have become widespread, and mobile communication technology continues to develop. These advances in network technology have enabled large numbers of devices to send and receive data simultaneously. By using a high-speed network, we are able to exchange information in real time with little communication delay.
As mentioned earlier, networks are essential for IoT systems. As network technology has evolved and data transmission and reception within IoT systems has become faster, convenience has improved. As a result, it is thought that the number of people who want to use IoT systems continues to increase.
Popularization of smaller devices
One of the reasons behind the spread of IoT systems is the spread of small and power-saving devices. Among the components of IoT systems, this is the development of devices.
Advances in industrial technology and science have made it possible to create small yet high-performance devices such as sensors and controllers. Small devices are more energy efficient than large devices, and have the advantage of being easier to use in household products. Advances in device miniaturization have made it possible to incorporate devices into a variety of products.
Even small devices can collect data in real time. It can be said that the progress made in achieving smaller size and higher performance of IoT devices is directly linked to the spread of IoT systems.
Manufacturing cost reduction through the development of advanced technology
The reason behind the spread of IoT systems is the decline in manufacturing costs due to the development of advanced technology. This is also a story about devices among the components of an IoT system. IoT devices have also become cheaper to manufacture due to advances in semiconductor technology, microprocessors, energy efficient device designs, and many other related technologies.
Furthermore, as an advanced technology, the application aspects of the components of IoT systems are also evolving. Advances in cloud computing and big data technology have also reduced the cost of data collection and processing.
The above two technologies have made it possible to manufacture high-performance IoT devices at low cost, and the convenience of the IoT system as a whole has improved, which is why IoT systems are becoming more popular.
What you can do with IoT systems
The following are examples of what can be done with IoT systems.
- Operations on things
- Monitoring the status and operation of things
- Communication between things
Operations on things
IoT systems allow users (humans) to operate things remotely.
For example, in a smart house, you can use your smartphone or tablet to adjust the temperature of your home’s air conditioner, turn on/off lights, and feed your pet. Smart cars also use mobile apps to unlock the car’s doors and start the engine.
By using IoT systems, it is possible to operate things via a network even when the user is not present.
Monitoring the status and operation of things
IoT systems can monitor the status and operation of things in real time. Devices such as sensors and actuators are embedded in products to collect and send data to applications and storage.
For example, a refrigerator with a built-in temperature sensor monitors the temperature inside the refrigerator and notifies the user if the temperature is abnormal. This not only allows users to notice abnormalities inside the refrigerator, but also helps prevent food from deteriorating if noticed early.
Let’s also consider the example of a smart meter installed as an electricity meter. Smart meters not only monitor electricity usage and notify the utility company, but also provide recommendations to users on how to improve their electricity usage based on the collected data and analysis results.
By monitoring the status and operation of things through IoT systems, it is possible to improve efficiency and safety, and discover problems early.
Communication between things
IoT systems allow things (products) to communicate with each other.
For example, in a smart home, sensors and smartphones communicate to share data and perform operations. In addition, in factories, manufacturing lines that use IoT systems allow machines to share data with each other. As a result, it will be possible to understand the details and progress of the preceding and succeeding processes, which will help improve operational efficiency and detect abnormalities.
The use of IoT systems allows things to communicate with each other, making life and work more efficient and convenient.

IoT system development flow
The general waterfall development flow for IoT systems is as follows.
- Requirements definition and design
- implementation
- quality test
- Release and maintenance operations
Requirements definition and design
Define requirements at the beginning of IoT system development. This is the process of clarifying the purpose and functionality of the system, working backwards from what users want, and understanding requirements and business needs.
Requirements definition clarifies what devices and sensors are needed, how data will be acquired and processed, network connectivity, security requirements, etc. In addition, the necessary budget, number of development team members, period, etc. are determined at the requirements definition stage.
Design is done after requirements definition. We provide technical details for the content documented in the requirements definition. Specific details include device architecture, data flow (network), user interface, etc.
Implementation
Once requirements definition and design are complete, implementation begins. We will actually create a pre-designed system as a program. The specific content to be implemented in an IoT system includes configuring devices and sensors, building network connections, and processing data. If you have any content that has been designed, such as cooperation with applications or cloud services, please implement it for those as well.
During implementation, we select an appropriate programming language and framework in advance, and optimize the programming itself from the standpoint of safety and performance.
Quality test
After the implementation is complete, we will conduct a quality test. Specific examples of quality tests include functional tests, performance tests, unit tests, and coordination tests.
Functional test: Check whether it can operate as required
Performance test: Evaluate whether it can withstand a certain load, communication speed, processing speed, etc.
Unit test: Check whether one function operates as expected
Linkage testing: Not only does it meet the specifications, but it also confirms that there are no abnormalities in areas where there were no problems in the unit test when multiple functions are combined.
If we determine that the quality does not meet the standards through quality testing, we will provide feedback to the design and implementation.
Release and maintenance operations
After passing the quality test, release your IoT system. At release, we will install and configure the system and create documentation for users.
After release, system maintenance and operation will begin. The goals of maintenance operations are to maintain system stability and performance, and improve user satisfaction. Specifically, we carry out periodic bug fixes and troubleshooting for the entire system.
It is also necessary to improve customer support. You may end up developing new features based on actual customer feedback.

Examples of IoT system introduction
We will introduce three examples of introducing IoT systems.
- Apartment that looks after seniors
- You can check the status of the trash can
Apartment that looks after seniors
Families who live far away from an elderly person often find it burdensome to visit them regularly to check on their condition. This condominium collects data on usage frequency and status by having customers use home appliances that incorporate an IoT system.
If an abnormality is detected in the data, a notification will be sent to the condominium management office, which will help prevent any accidents. By using the IoT system, we are contributing not only to families in need of nursing care, but also to the nursing care industry.

You can check the status of the trash can
Nippon Systemware has developed “BigBelly Solar,” a trash can that incorporates IoT. The collection status of garbage accumulated in the trash can is visualized, making garbage collection more efficient. It also has an automatic garbage compaction function, so you can check the accumulation status and have the advantage of preventing garbage from overflowing from the trash can.
By visualizing the collection status of each trash can, you will be able to see which trash cans are used more frequently and which ones are less frequently used, and you will be able to more efficiently consider areas where you should increase or decrease the number of trash cans.
This is an example that shows how using IoT devices can help solve familiar problems.
Sunstar has developed G・U・M PLAY, a device that works with a smartphone to record tooth brushing status. By incorporating a sensor into your toothbrush, you can score your tooth brushing based on the movement of your toothbrush and the time you brush.
Users can use a smartphone app to view the data collected by the toothbrush and understand how well they are brushing their teeth. The app analyzes your tooth brushing data and gives you advice on how to best brush your teeth.
This is an example of using an IoT system to visualize something that you cannot understand even if you do it yourself.
Introduction to IoT system certification
From here, we will explain the following two contents about the “IoT System Technology Certification Exam”, an exam that can check the development and knowledge of IoT systems.
- Overview of IoT System Certification and Benefits of Taking the Test
- Skills and knowledge gained through IoT system certification
Overview of IoT System Certification and Benefits of Taking the Test
The exam has three levels: advanced, intermediate, and basic. Only the advanced level requires exam qualification, and you must have passed the intermediate level or taken a certification program. The exam method is basic, with the intermediate level being a CBT exam (multiple choice questions), and the advanced level being an essay exam based on the course content and course content.
If you are a beginner in IoT systems or are taking the test for the first time, it is recommended that you take the test from the basic level. The purpose of the IoT System Certification is to develop IoT engineers, and each exam requires a certain level of knowledge.
The benefit of passing the test is that you will have more opportunities to work in businesses related to IoT system development (including sales, etc.). Having a qualification will show off that you have knowledge, which can also be used when looking for a job.

Skills and knowledge gained through IoT system certification
The skills gained through the IoT System Certification are the knowledge that is appropriate to the level of each exam. For example, at the basic level, the content would be as follows.
- IoT system configuration and construction technology
- Sensor/actuator technology and communication method
- IoT data utilization technology (AI)
- IoT information security measures technology
- IoT system prototyping technology
There are some parts that overlap with the content explained in this article. The exam will ask you about more detailed information, so it is a good idea to prepare for the exam using a question set.
summary
We provided an overview of the IoT system, its components, the background of its spread, what it can do, the development flow, implementation examples, and IoT system certification.
The number of household appliances that use IoT systems is increasing every day, and this trend is expected to continue in the future. In order to coexist with IoT systems in your life, it is important to understand the basics explained in this article.

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May 26, 2024