10 Best Practices for Implementing Automated Web Data Mining using BOTS

10 Best Practices for Implementing Automated Web Data Mining using BOTS A famous manufacturer of household products, working with a number of retailers across the globe, wanted to capture product reviews from retail websites. The objective was to understand consumer satisfaction levels and identify retailers violating the MAP (Minimum Advertised Policy) policy. The manufacturer partnered with a web scraping and distributed server technology expert to get an accurate, comprehensive and real-time overview of their requirements. It took them no time to get complete control over the retailers and pre-empt competitors with a continuous sneak peek into their activities. This example underscores the importance of web scraping as a strategic business planning tool.

Web scraping is the process of extracting unique, rich, proprietary and time sensitive data from websites for meeting specific business objectives such as data mining, price change monitoring contact scrapping, product review scrapping and so on. The data to be extracted is primarily contained in a PDF or a table format which renders it unavailable for reuse. While there are many ways to accomplish web data scraping, most of them are manual, and so, tedious and time-consuming.

However, in the age of automation, ​automated web data mining has replaced the obsolete methods of data extraction and transformed it into a time saving and effortless process.

How is Web Data Scraping Done Web data scraping is done either by using a software or writing codes. The software used to scrap can be locally installed in the targeted computer or run in Cloud. Yet another technique is hiring a developer to build highly customized data extraction software to execute specific requirements. The most common technologies used for scraping are Wget, cURL, HTTrack, Selenium, Scrapy, PhantomJS and Node.js.

Best Practice for Web Data Mining 1) Begin With Website Analysis and Background Check

To start with, it is very important to develop an understanding about the structure and scale of the target website. Extensive background check helps check robot.txt and minimize the chance of getting detected and blocked; examine the sitemap for well-defined and detailed crawling; estimate the size of the website to understand the effort and time required; identify the technology used to build the website for seamless crawling and more.

2) Treat Robot.txt -Terms and Conditions The robots.txt file is a valuable resource that helps the ​web crawler eliminate the chances of being spotted, as well as uncover the structure of a website. It's important to understand and follow the protocol of robot.txt files to avoid legal ramifications. Complying with access rules, visit times, crawl rate limiting, request rate helps to adhere to the best crawling practices and carry out ethical scrapping.

Web scraping bots studiously read and follow all the terms and conditions​.

3) Use Rotating IPs and Minimize the Loads More number of requests from a single IP address, alerts a site and induces it to block the IP address. To escape this possibility, it's important to create a pool of IP addresses and route requests randomly through the pool of IP addresses. As requests on the target website come through different IPs, the load of requests from a single IP gets minimized, thereby minimizing the chances of being spotted and blacklisted. With ​automated data mining​, however, this problem stands completely eliminated.

4) Set Right Frequency to Hit Servers In a bid to fetch data as fast as possible most web scraping activities send more number of requests to the host server than normal. This triggers suspicion about unhuman-like activity leading to being blocked. Sometimes it even leads to server overloads causing the server to fail. This can be avoided by having random time delay between requests and limit page access requests to 1-2 pages every time.

5) Use Dynamic Crawling Pattern Web data scraping activities usually follow a pattern. The anti-crawling mechanisms of sites can detect such patterns without much effort because the patterns keep repeating at a particular speed. Changing the regular design of extracting information helps to escape a crawler from being detected by the site. Therefore, having a dynamic ​web data crawling pattern for extracting information makes the site's anti-crawling mechanism believe that the activity is being performed by humans. ​Automated web data scraping​ ensures patterns are repeatedly changed.

6) Avoid Web Scraping During Peak Hours Scheduling web crawling during off-peak hours is always a good practice. It ensures data collection without overwhelming the website's server and triggering any suspicion. Besides, off-peak scrapping also helps to improve the speed of data extraction. Even though waiting for off-peak hours slows down the overall data collection process, it's a practice worth implementing.

7) Leverage Right Tools Libraries and Framework There are many types of web scraping tools. But it's important to pick the right software, based upon technical ability and specific use case. For instance, web scraping browser extensions have less advanced features compared to open-source programming technologies. Likewise smaller web data scraping tools can be run effectively from within a browser, whereas large suites of web scraping tools are more effective and economical as standalone programs.

8) Treat Canonical URLs Sometimes, a single website can have multiple URLs with the same data. Scraping data from these websites leads to collection of duplicate data from duplicate URLs. This leads to a waste of time and efforts. The duplicate URL, however, will have a canonical URL mentioned. The canonical URL points the web crawler to the original URL. Giving due importance to canonical URLs during the scrapping process ensures there is no scraping of duplicate contents.

9) Set a Monitoring Mechanism An important aspect of ​web scraping bots is to find the right and most reliable

websites to crawl. The right kind of monitoring mechanism helps to identify the most reliable website. A robust monitoring mechanism helps to identify sites with too many broken links, spot sites with fast changing coding practices and discover sites with fresh and top-quality data.

10) Respect the Law Web scraping should be carried out in ethical ways. It's never right to misrepresent the purpose of scrapping. Likewise it's wrong to use deceptive methods to gain access. Always request data at a reasonable rate and seek data that is absolutely needed. Similarly, never reproduce copyrighted web content and instead strive to create new value from it. Yet another important requirement is to respond in a timely fashion to any targeted websites outreach and work amicably towards a resolution.

Conclusion

While the scope of web data scraping is immense for any business, it needs to be borne in mind that data scraping is an expert activity and has to be done mindfully.

The above mentioned practices will ensure the right game plan to scrap irrespective of the scale and challenges involved.

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