Thursday, 30 April 2020

Brief Introduction to The Internet of Things and An Use Case

What is the Internet of Things?
The Internet of Things is often abbreviated as IoT, which refers to the connection of devices (except computers and smartphones) to the Internet. Cars, home products, and medical supplies can all be connected within the Internet of Things. And with the development of the Internet of Things in the next few years, more types of devices will be connected.

IoT devices, Platforms and IoT ecosystem

IoT devices and IoT ecosystem
Any independent Internet-connected device that can be monitored and controlled remotely is considered an IoT device. The core technology of IoT devices is chips. These chips are smaller and more powerful.
All devices connected to the Internet and the data collected through these devices, including data storage, analysis, processing, and security functions, constitute the IoT ecosystem.

IoT forecasts, trends and markets
There will be more than 64 billion IoT devices installed around the world by 2026. Additionally, companies and consumers will spend nearly $15 trillion on IoT devices, solutions, and supporting systems from 2018 through 2026.

The three types of consumers of the Internet of Things include governments, enterprises, and ordinary consumers. The Internet of Things will affect almost every industry. This continuous growth will become a transformative force for all organizations.

"By 2026, the Internet of Things market is expected to grow at a rate of more than US $ 3 trillion per year."

Internet of Things platform
In the Internet of Things, one device is connected to another device to transmit information using the Internet transmission protocol, and the Internet of Things platform acts as a bridge between the device sensor and the data network.

These powerful IoT platforms can analyze to determine what information is useful and which can be ignored. If the enterprise uses it for sales management, it can more accurately predict products and effectively manage inventory, make the use of capital more reasonable, and can also automate certain tasks, especially when these tasks are repetitive and time-consuming or even dangerous .

These IoT platforms include

Amazon Web Services
Microsoft Azure
ThingWorx IoT platform
IBM Watson
Cisco IoT Cloud Connect
Salesforce IoT Cloud
Oracle Integrated Cloud
General Electric Predix

Internet of Things Security and Privacy
More and more devices will be connected to the Internet of Things, these devices collect a lot of data, these data include business, as well as individuals, business secrets and personal privacy are greatly affected by the challenge, so the Internet of Things security issues are subject to with great attention, there are already some technology companies focusing on cybersecurity, and some universities and vocational training schools provide cybersecurity courses and training. Cybersecurity experts will be an emerging profession with broad employment prospects.

An IoT application scenario

At home:
Suppose you usually go to work at 9 AM, you set an ordinary alarm clock at 8 AM, and the time on the road is one hour, then under normal circumstances you can arrive on time. The rain changed on this day, the road was blocked, and traffic was slow. If you still go out at the usual 8 AM, then you are destined to be late. But if you use the alarm clock connected to the Internet of Things, the situation will be different, he will wake you up in advance, help you choose the best traffic section, and guide you to work. If you take public transportation, buses, trains, airplanes, it will help you know whether they are operating normally, or whether they are cancelled or delayed due to weather conditions. It even suggests you which type of transportation is the most time-saving.

In conclusion:

The convenience provided by the Internet of Things for consumers is everywhere, which is why the Internet of Things will flourish. Both businesses and individuals must seize this great opportunity.

#IoT #bigdata #Cybersecurity

9 Trends in IoT Mobile App Development

The use of smart phones around the world has become very popular, and the Internet of Things is also booming in the continuous increase, which makes mobile applications become the preferred channel for accessing the Internet of Things. The mobile platform is used to transmit data, and it is more convenient. The program plays an important role in promoting the growth of the Internet of Things. For beginners who want to learn the design and development of Internet mobile applications, the following trends should be grasped.

New trends in IoT mobile application development

Smart home equipment
IoT home devices will be more comfortable, safe and energy-saving.

Edge computing
Compared to cloud computing, edge computing has multiple advantages, being able to better manage the large amount of data sent by each device, reducing the dependence on the cloud, helping applications execute faster and reduce latency. The program consumes less traffic and will be innovative and widely adopted in the Internet of Things.

The healthcare industry will widely use the Internet of Things
According to relevant predictions, the medical Internet of Things is expected to grow at a compound annual growth rate of 26.2%, and will reach 72 billion US dollars by 2021.
Devices such as sensors, portable devices, medical devices, health monitors, and a range of other medical devices are all configured to connect to the Internet of Things. Mobile health applications and virtual assistants enable remote monitoring, which includes the patient's condition and signs at home and in the car.

IoT security will be strengthened
Due to the universal application of the Internet of Things, various privacy-related information is transmitted in the network, and people's requirements for the security of the Internet of Things are increasing day by day. The development of mobile applications based on the Internet of Things will be more secure than ever. These technologies include machine-to-machine authentication, biometric login, etc. Artificial intelligence, machine learning, and big data technologies will be used to identify and prevent data violations.

Artificial intelligence, big data and data analysis
As more devices are connected to the Internet of Things, massive amounts of data will be generated. After processing and analysis, these data will have a positive effect on enterprises and individuals and provide better decision-making. The convergence of artificial intelligence, the Internet of Things and big data will spawn a new generation of applications.

Smart cities become a trend
Due to the large number of sensors deployed and the development of related technologies, cities will become smarter. The most typical is that people can control traffic flow more reasonably and effectively, reduce city congestion, and improve safety.

Personalization of the retail experience
The Internet of Things makes today's retail supply chain management more efficient. Merchants can obtain data through the Internet, analyze consumer habits and preferences more accurately, provide accurate product information, discount information, etc., so that consumers have a better retail experience.

IoT predictability maintenance
In the most typical case, a house connected with Internet of Things equipment will notify the homeowner about pipeline leaks, equipment failures, circuit problems, etc., so that the homeowner can repair in time to avoid the occurrence of larger problems. No matter where the owner is, they will be notified in time.

Energy and resource management
The Internet of Things technology can be integrated into resource management, including sprinkler control, indoor temperature management, etc.

In conclusion

The wide application of the Internet of Things technology has caused tremendous changes in all aspects of production, consumption and life. Whether it is a company or an individual, to enjoy these changes, it is only necessary to open the mobile phone and easily manipulate mobile applications.

For mobile application developers, these trends should be mastered in order to better satisfy consumers.

#IoT #artificialintelligence #bigdata #datascience

Tuesday, 28 April 2020

Brief Introduction of Big Data, Data Science and Data Analysis

The terms big data, data science, and data analysis have become more popular and are becoming the next wave of scientific and technological trends. So what is big data, what is data science, and what is data analysis, and how will it affect business, there is a brief introduction here.

Big Data and Basic Characteristics - 5V 

Big Data has five basic characteristics, and the nouns that summarize these characteristics all start with the English letter V, referred to as Big Data 5V.

Whether data is valuable is directly related to its quantity. Whether certain data can be considered as big data, quantity is the first factor to be considered.
Current amount of data on the web:
In 2016, the estimated global mobile traffic is 6.2 Exabytes (6.2 billion GB) per month. By 2020, we will have nearly 40,000 ExaBytes of data.

Data appears at a faster and more continuous speed, because many applications are generated based on the web. Example: There are more than 3.5 billion searches on Google every day, and on YouTube, about 300 hours of video are uploaded every minute.

The sources and types of data are different. The data includes various files, tables, images, videos, audio etc. They are structured, semi-structured and unstructured data.

Structured data: refers to data with a fixed format or limited length, such as a database.
Semi-structured data can be processed as structured data as needed, or plain text can be extracted and processed as unstructured data, such as HTML.
Unstructured data: refers to data of indefinite length or no fixed format, such as mail, text, images, videos, etc.

Data comes from different data sources, including different types of data, and the quality and accuracy are difficult to control. Large amounts of data may cause confusion, too little data will convey incomplete information, so companies need to obtain real and reliable data.

Based on the web, especially the Internet of Things generates a lot of data. These large amounts of unprocessed data are of little value to the company, which requires powerful machine algorithms to discover its value.

Source :  

the difference between Data Science, Data Analysis, Big Data, Data Mining and Machine Learning 

What is data science and data analysis

As mentioned earlier, data needs to be processed before it can generate value.

Data science - It is the study and processing of data so that they can bring meaningful insights to individuals or businesses.

Data science is the study of data, which includes methods and tools. The data under study can be in the form of Big Data.

The skills required for data science include mathematics, statistics, and related business knowledge.

Data analysis - It is a specified quantity and statistical method. Data science is a collective term, and data analysis is a method that is part of data science.

The value of data analysis to enterprises is to reduce costs, accurately predict, and assess risks.

in conclusion:

With the vigorous development of the Internet of Things in the future, commercial companies will collect large amounts of data. Effectively process and analyze these data, which will provide strong support for the company's decision-making and development.

#BigData #Data analysis #Data science #IoT #InternetofThings

5 Types of Artificial Intelligence Commonly Used in Business

According to this article, 5 Types of AI to Propel Your Business,

There are about 5 types of artificial intelligence commonly used by companies.

What is artificial intelligence and the value of commercial artificial intelligence

Definition of artificial intelligence

In short, artificial intelligence is a new technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. It is a branch of computer science.

In the next 5 years from now, about 90% of enterprises have or are trying artificial intelligence. The value expected for investing in artificial intelligence includes the following

1. Improve product performance

2. Make more accurate decisions

3. Optimize internal and external operations

4. Make accurate assessments for developing new markets

The 5 types of artificial intelligence commonly used commercially

At present and in the next few years, the artificial intelligence commonly used in business generally includes the following five types.

Analytical Type AI

Functional Type AI

Interactive Type AI

Text Type AI

Visual Type AI

Here is a brief introduction one by one

Analytical Type AI
The tools used by analytical artificial intelligence are machine learning and deep learning, which are used to quickly scan and analyze massive amounts of data, and ultimately use the data as a benchmark to provide recommendations for corporate decision-making. This helps to predict market demand more accurately, manage inventory more reasonably, and use funds effectively.

Functional Type AI
The similarity between functional and analytical artificial intelligence is that it also analyzes and scans massive amounts of data. The difference is that it does not provide advice but takes action. For example, in warehouse management and cargo picking, the speed and accuracy can be greatly improved. Currently, Amazon is already using it. In the near future, more online retail companies will use it to strengthen warehouse management operations.

Interactive Type AI
Currently the most developed interactive AI is chatbot. This type of artificial intelligence is used to strengthen the operation process of many departments to automate them, thereby greatly reducing repetitive work and waiting time, and significantly improving customers satisfaction, the departments or businesses that most often use this artificial intelligence are customer service, online retail, reservations, etc.

Text Type AI
The core technology adopted by this artificial intelligence is semantic search and natural language processing. The functions that can be achieved include the conversion of speech and text. It can also be used to support the company's internal knowledge base, construct language intentions, identify synonyms, etc. It effectively reduces the cost and time of manual input, improves input efficiency and accuracy.

Visual Type AI
It is to transform jerky data into vivid images, and can convert images and videos, which helps enter
prise personnel to improve understanding, reduce analysis time and improve accuracy when dealing with specific problems, it makes a lot of operational convenience. It can also provide facial recognition solutions to provide a better customer experience and improve security for the retail industry.

in conclusion

With the rapid development of artificial intelligence technology, more types of artificial intelligence will be applied in the commercial field, thereby more effectively improving business operations, providing consumers with better services, and creating higher revenues for commercial companies.

#Artificial intelligence #business #Retail #Customer service #Business analysis #Inventory management

Source : 5 Types of AI to Propel Your Business

Friday, 24 April 2020

IoT-Based Mobile Apps Benefits for Businesses And Consumers

Internet of Things Application Prospects

Gartner predicts that by 2020, about 20 billion devices will be connected to the Internet of Things, and IoT product and service providers will increase business revenue by $ 300 billion. There is no doubt that the connected world of the Internet of Things is developing rapidly. Now, everyone is looking for the future of the Internet of Things and how it will affect our lives in the near future. Experts predict that by 2025, there will be more than 64 billion connected IoT devices.

With the popularity of smartphones worldwide, the Internet of Things is positioning itself as entering the field of mobile apps, and the Internet of Things will inevitably have a new impact on the mobile user experience.

Reasons for the growth of IoT mobile apps

The popularity of smartphones makes it an important device for the Internet of Things.

For IoT, mobile applications can flexibly transmit data, and IoT mobile application development can easily be added to existing IoT systems.

What the Internet of Things can offer consumers and businesses by leveraging mobile applications


The Internet of Things enables people to understand, observe and analyze with equipment without manual operation or human intervention, saving a lot of labor costs.

In-depth understanding of consumer behavior

With the help of the Internet of Things, enterprises generate relevant information, data, etc. about their customers through mobile applications, social media, video surveillance, GPS tracking and other resources. By better understanding customers and their habits, and further optimizing the mobile application experience, you can even satisfy the most critical customer base.

Increase productivity

With the help of the Internet of Things, companies can manage employees more efficiently, such as optimizing working hours, rest time, meeting time, etc.

Improve customer experience

The Internet of Things enables enterprises to provide more convenient service processes, from payment processing, confirmation, delivery, customer service, arrival confirmation, and even to return, refund, and claims.

Convenient and safe workplace

From the company's provision of smart locks to remote monitoring and even immediate alarms, it can provide employees with more convenience and safety.

More efficient data environment

IoT devices are connected to the entire IoT system and can generate large amounts of data. With Edge Computing, metrics can be reported in real time, and this speed provides companies with more accurate results for understanding customers.

In conclusion

The Internet of Things technology will continue to develop and popularize. Enterprises must adapt and implement the Internet of Things as soon as possible to maintain competitiveness. The Internet of Things is rapidly becoming one of the main driving forces for mobile application development.

# IoT #InternetofThings #Mobile Application #Business #Enterprise #Data

Thursday, 23 April 2020

Automated Machine Learning Promotes Demand for Data Analysts and Easier Access

The following are the two major trends in the field of data science in the next decade

It's getting easier for companies to get data

Increasing demand for data analysts

The World Economic Forum predicts in 2019 that by 2020, the demand for data analysts will be large, and so far this year, we have seen this forecast become a reality.

It's getting easier for companies to get data

This is because many companies have invested in the results of Automated Machine Learning (AutoML), which enables them to apply machine learning automatically to solve business challenges. That is to say, in a general company, ordinary data analysts can access and use data more freely, instead of having difficult access to data analysis like the company's business leaders in the past, data scientists must be required to provide specific reports and analysis on a case-by-case basis , Which greatly improves the efficiency of enterprises to obtain data and reduces costs.

With the rapid development of business and technology, more and more people and enterprises need to freely access data sources, models provided by data, and data-driven analysis. This is the reason for people and enterprises to turn to Automated Machine Learning (AutoML).

Modern companies want to see the business status of data-driven analysis, not just limited to the scope of data science laboratories. This trend is called Democratizing Data. Data Democratization makes it easier for data analysts to get involved in data. They can guide themselves through function creation, function selection, model creation and comparison, and even operations, which can improve the efficiency of obtaining large amounts of data.

Increasing demand for data analysts

In the last one or two years, one of the most successful operational changes in technology-driven business is the continuous integration of data science and business intelligence. The company has effectively realized real-time, centralized access to completely different data sources. This makes more people become data analysts. The new type of data analysts combine the technical expertise of data scientists with operational expertise in marketing, supply chain, manufacturing, risk, and other industries. Data analysts do not need to master Machine Learning, but they have a deeper understanding of their company's problems.

In Conclusion

The next step in development will be to make Machine Learning more self-service, easier, and automated.

By integrating self-service machine learning into its core business strategy, innovative companies enable data analysts to use real-time data on a large scale to make better and faster decisions throughout the organization.

#Big Data #data analysis #data science #data analyst #AutomatedMachineLearning #AutoML #machinelearning

Source :   The new decade and the rise of AutoML

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