The quantity of data available on the web is consistently growing. Companies fetch, process and clean this data to make decisions, particularly after the boom in the AI and the machine learning fields, which nowadays require enormous amounts of data for training their systems. Part of this data is available via public APIs (1). Yet, the majority of data is unstructured, still only available through the use of web crawling and web scraping. In this post, we will focus on what scraping is and how it is done nowadays.
Definition of scraping
Web scraping can be defined as the process of extracting information from a website. Technically, this extraction process can be done manually by a human user, and although this is a very slow and tedious process, is still the go-to approach by many companies when they need to get data from the web, such as copying and pasting information from the web to an Excel file.
The pros of this approach are that we can leverage human intellect and expertise to extract (or even infer) very specific information from the web; for example, a human expert in finance can extrapolate information from a company given the news articles that mention it.
One of the cons of this method of extracting information is that even the fastest human worker cannot outperform a machine in doing mechanical and repetitive tasks such as copying information from a website and storing it. To overcome this limitation semi-automatic scraping was developed. Another problem is that the data extracted is prone to be incorrect due to human error.
This approach is based on the use of an automated web browser or a bot to automatically extract—and sometimes process—the data from any given website. A problem that can be solved with this method can be the following: given a list of URLs from several companies, extract their contact information such as their address and/or phone numbers.
Under the hood what is happening is that a human is configuring the bot to extract the information according to their needs.
We can observe that human intervention is still needed in the scraping workflow. However, this time the human allocates a fraction of its time to configure the bot once in how to extract the data and then the bot does the rest. Once configured, the machine can extract the information automatically, orders of magnitude faster, in a more stable way and is less prone to errors compared with the human worker.
What does the future hold?
With the rise of artificial intelligence in all the industries, fully-automated solutions are being developed both in research and commercially. Here at Plyzer Technologies, we scrape millions of URLs daily, so it is not just interesting to develop automated and efficient solutions for our clients, it’s a must. With our machine learning and artificial intelligence-based solutions, we are continuously creating tools both for internal usage and for client projects that let us achieve our professional objectives.
We will post more about the use of AI in scraping in future posts. Stay tuned for more!
Footnotes(1) API: Application Programming Interfaces which, simply put, allow people to obtain structured data easily.