Hello, this is Hashscraper!
Starting the second post on crawling technology following Part 1!
Crawling Technology 3: Virtualization in the Cloud
To explain this technology, a little explanation about 'cloud computing' and 'virtualization' is needed.
We will share an easy-to-understand post about 'What is the cloud' for basic introductory knowledge.
For those who are not familiar with computers! It will be easier to understand if you read the post below :)
In short, cloud computing is 'implementing IT services without physical equipment'.
You can use the internet to access services without installing/operating/managing servers and network equipment.
Even if traffic suddenly increases, server usage fees are calculated on an hourly basis, preventing waste of computing resources (cost).
Also, it is easy to collect large amounts of data due to its scalability, making it ideal for crawling companies like us.
However, only a few companies are using cloud services. Why is that?
In the end, it's because of cost. (Chose the cloud because of cost, but have to abandon it because of cost..ㅠㅠ)
Storing a large amount of data in the cloud and reading it back requires significant network bandwidth, which costs quite a bit.
If a company's IT resources grow, using an internal cloud would be more cost-effective.
Therefore, the method we found is 'virtualization in the cloud'.
Although you may have heard of 'virtualization', for those unfamiliar with 'cloud virtualization', let me explain.
Virtualization is a technology that separates functions from hardware devices.
It can make one device operate like multiple devices or vice versa, combining multiple devices to provide as if they were one.
Hashscraper has applied virtualization technology to various computing resources.
By applying virtualization technology to hardware owned by cloud services such as AWS (Amazon Web Service), GCP (Google Cloud Platform), and IDC (Internet Data Center), as well as hardware owned internally, they can run simultaneously!
AWS, GCP, IDC, physical HW, etc., different types of computing resources are virtualized by Hashscraper's server management system, allowing multiple virtual machines to be managed and perform tasks according to the collection purpose and situation.
In a virtualized environment like this, computing resources can be quickly switched depending on the collection purpose and data volume, or multiple tasks can be performed simultaneously.
You can collect data using virtualized AWS and IDC, then switch to GCP and Hashscraper HW for collection.
If AWS's IP is blocked and collection is not possible, you can use a proxy server to switch from AWS to IDC.
(Sometimes there are sites that block specific cloud service IPs. To prepare for such situations, Hashscraper has various computing resources.)
This way, you can find the most cost-effective and optimal way to collect data according to the situation, which does not incur high maintenance costs.
Although it may be somewhat complex and difficult, 'cloud virtualization' server operation technology is a key technology that can save your costs :D
Crawling Technology 4: Machine Learning Technology
Machine learning is hot these days.
Machine learning technology is also widely applied in crawling.
The machine learning technologies we mainly use include natural language processing, image analysis, etc.
By using these technologies, we can improve the quality, accuracy, and speed of data.
Let me give you an easy example.
Do you always check product reviews on online shopping malls before making a purchase?
That's why many online shopping mall owners want to collect and analyze product reviews. They want to sell only products with good reviews.
However, these owners are too busy. When will they collect and analyze all the reviews one by one? That's where we come in :D
Analyzing comments by breaking them down into pieces is called natural language processing.
You can check the positive/negative rate of a product or learn about its characteristics.
If you have specific features in mind, you can analyze based on those features and score products accordingly.
By scoring products, you can easily see which products have the best design or the highest satisfaction for the price.
When this information accumulates and algorithms are created, you can even predict sales volume.
(Actually, a customer from Company S collected and analyzed a lot of data for sales prediction.)
Sometimes customers want to know if the products they are selling are also being sold elsewhere, and if so, at what price.
In that case, image analysis technology comes into play. By weighting the characteristics of images, it determines if they are similar.
By analyzing colors, shapes, etc., of the clothes you are looking for, if a certain percentage matches, it is considered a similar or identical image.
Through this image analysis, you can find similar products or create a service that recommends products with similar styles.
Applying artificial intelligence and machine learning technology to data collection/analysis enables informed decision-making based on accurate information, increasing efficiency and cost savings. Therefore, many companies worldwide are eager to apply artificial intelligence and machine learning technologies to analyze data.
However, the cost is very high.. But Hashscraper provides machine learning technology at an affordable price.
If you ask why,
As mentioned earlier, with the 4 crawling technologies, it is possible to save on labor costs and server costs, making it possible to provide services at a lower cost.
So far, I have explained Hashscraper's crawling technology.
I'm not sure if the reason for free maintenance has been well conveyed.
If you have any difficulties or questions, please feel free to contact us via ChannelTalk anytime.
Hashscraper's mission is to provide a service that allows anyone, anywhere, to easily request and utilize data with minimal cost and effort.
We prioritize the development of technology and services to reduce the cost of data collection as our primary value.
Please watch how well Hashscraper upholds this value
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