Web scraping is usually regarded as data mining and knowledge discovery. It is the process of extracting useful data and relationships from any data sources. For instance the web pages, databases and search engines. It employs pattern matching and statistical techniques. It is important to note that web scraping does not borrow from other fields like machine learning, databases, data visualization and others but supports such fields.
Web scraping process is such a complex process that requires not only time but also people with expertise in the same field. This is because the internet is such a dynamic resource that changes every time. For instance, the data you can extract from a certain website a month ago will not be the same one you will extract now. The changing of data in short period of time poses the difficult of relying on such data and therefore calls for web scraping process. The web scraping process should be performed regularly in order to obtain accurate data that can be relied upon.
It is important to understand that many areas of business, science and other environments use a large amount of data. This data needs to be meaningful and knowledge in its application. Web scraping sometimes may be overlooked, but in essence, it can provide very useful information than the statistical methods can produce. The web scraping methods are vital as they give you more control over the data.
Usually, the data found on the internet is noisy data. This implies the advertisements and pop-ups. The data also found on the internet can be described as dynamic data, sparse data, static data, heterogeneity and so and so forth. Such problems occur in very large amounts and therefore call for web scraping professional companies to perform their job. With such problems, it is important to realize that statistical methods would never succeed and therefore calls for web scraping.
The process of web scraping
1. Identification of data sources and selection of target data. You need not to harvest any kind of data, but data that is deemed relevant and useful in its application. The relevance can be seen in a way of getting the data that will benefit your company. This is an important step in the web scraping process.
2. Pre-process. This involves cleaning and attributes selection of data before it is being harvested. Web scraping is usually done on specific websites that are relevant to your business. For instance, if you have an online store and need information about your competitor’s products then you need data from other websites that are relevant such e-commerce stores and so on.
3. Web scraping. This involves data mining so as to extract models and information patterns or models that is beneficial to your business.
4. Post-process. After web scraping is done, it is important to identify the useful data that can be used in your business in decision making and so on.
It is important to note that the patterns identified need to be novel, understandable, potentially viable and valid for web scraping process to make sense in business data harvesting.