With the drastic increase in digital transformation, the internet houses humongous sets of data that can get difficult to manage. Today, there are myriads of tools and techniques evolving for efficient data management.
Ever wondered how a giant search engine like Google collects data to display in the SERPs (search engine results pages)? Does it use a web crawler to retrieve data faster? In this post, we are going to see how using a web crawler can be beneficial for data science.
What Is A Web Crawler?
A Web crawler, also known as a web robot, a web spider or a spider bot, is an automated script or program that logically browses the internet. This automated process of indexing data on web pages is known as web crawling or spidering. Myriads of search engines such as Bing, Google, and etc. use web crawlers to provide up-to-date information in SERPs.
The core objective of a web crawler is to create a copy of all the visited pages, which are later processed by the search engine. The search engine will then index these downloaded pages to return the results faster in SERPs. Their main task is to learn what web pages comprise, so as to return relevant information when searched by users.
Crawlers also do other automated tasks related to website maintenance such as validating HTML code, checking links, etc. Additionally, web crawlers can be used to collect email addresses or other types of information from users visiting the web pages.
Why Is Web Crawling important?
Credit to the evolving digital era, the amount of data on the World Wide Web reached 40 zettabytes in the year 2020. A zettabyte is about a billion terabytes or a trillion gigabytes. The important thing to note is such a massive amount of data is bound to be unstructured and difficult to manage. Web crawling is important to index such large, unorganized data to help search engines return relevant results to the users.
Some of the key features of a web crawler include:
- Robust build: The internet comprises powerful servers which may set out web page generators and spider traps. These may make it difficult for web crawlers to collect web pages. Hence, the web crawler must be capable and robust enough to handle such complex traps.
- Precision: the web crawler must be able to precisely return relevant and important results as per the users’ search.
- Distributed: the web crawler must be able to run on distinct machines without any glitches.
- Extensible: the web crawler must be flexible enough to easily adapt to new environments, protocols, and features.
How Does A Web Crawler work?
Web crawlers initiate their automated process by downloading the robot.txt file of the website. This file includes sitemaps with a list of all the URLs that the relative search engine can crawl. Through these links, web crawlers are able to discover new web pages as they begin to crawl. Regularly, the crawlers keep adding newly discovered URLs to the queue to be crawled later on. With such an approach, web crawlers begin indexing every page linked to the others.
Web pages tend to change every now and then, hence it is important for search engines to know how often to crawl them. For this, crawlers use various algorithms to determine factors such as how many web pages to index on a website, how frequently a page should be crawled again, and so on.
How Do Data Scientists Collect Data?
Data scientists have different ways to gather data from the internet. Some of them are as follows:
- Look for an existing dataset:
- Use public datasets: There are numerous datasets on the internet to be used as a benchmark for general computer science problems and measure their accuracy such as image recognition.
- Purchase datasets: There are various online platforms and marketplaces where you can buy datasets such as environmental data, political data, customer data, etc.
- Company’s datasets: Companies can easily access their own data stack.
- Create a new dataset:
- Create data manually: Data scientists can manually create online surveys to gather results. Or, they can use old surveys and their results or pay employees to perform manual tasks of data classification and data labeling.
- Convert existing data into a dataset: Another great way to gather data from the internet is by crawling websites and downloading public data. This can be done via dedicated web crawlers or manually through RPA bots that are programmed for web crawling.
Use Of Web Crawlers In Data Science:
Web crawlers are applicable in Data Science for various niches. Some of the key applications include:
- Real-time analytics:
Websites and online platforms on the internet are crucial sources of real-time data. And, data science projects need real-time data for analytics. Such data can be collected by crawlers via high-frequency data extraction.
You can program web crawlers to automatically crawl data from specific websites at preset time intervals such as every month, week, day or hour. As per the requirements of the data science projects, data scientists can acquire data in real-time for better decision-making.
For example, data about recent natural disasters such as storms, volcanoes, tsunami, etc. can be crawled from social media, news websites, tweets, online updates from government sites, etc. Such data crawling helps data scientists or government officials analyze the scenario and take immediate actions.
- Predictive modeling:
Predictive modeling, also referred to as predictive analytics, is all about creating an AI (Artificial Intelligence) model that is able to detect patterns and behavior in historical data and classify events as per their frequency. With this, the model can predict the probability of an event occurring in the future.
Now predictive models may need large sets of data in order to derive precise results. Hence it is very important for data scientists to choose apt web crawlers for data extraction, instead of performing it manually.
Within the model, there are various predictors that manipulate the end results. Web crawlers can collect the essential information from websites which you can use to develop a precise predictor.
- Optimize NLP (Natural Language Processing) models:
Natural Language Processing sits at the core of interactive AI applications. However, NLP may experience various bottlenecks due to unpredicted, complex human speech in the form of ambiguity, sarcasm, abbreviations, and so on.
Optimizing NLP models are applicable for machine equipment that can interpret. The Internet consists of highly versatile and large amounts of data consisting of human speech in numerous languages, sentiments and syntaxes. Such data can be crawled to gather extensive, up-to-date training data to create NLP (natural language processing) and conversational AI models. For example, NLP can use text in e-commerce site’s data, blogs, tweets, user reviews, etc.
- Training ML models:
Training datasets help ML (machine learning) models to execute data classification, clustering, and other such tasks.
In today’s world, web crawlers are quite a buzz, especially in the field of data science. Humongous sets of data present on the World Wide Web tends to be unstructured and complex to manage. However, web crawlers open the doors of numerous opportunities to pave the way through data science.
Data scientists can leverage web crawlers to execute real-time data analytics, train predictive ML models, and improve capabilities of NLP models and so much more.
Also Read: How To Manage AI Data Sources