Things You Should Know About Web Data Mining, Its Purpose & Process
It’s quite fascinating how we create a pattern through our digital footprint when we set preferences of what we surf, like or dislike on the internet. We browse, upload, download, and comment on various internet platforms regularly, and that’s how we create our digital footprint. Based on the pattern we create, we are catered content on any social media platform we visit regularly. Ever wondered what sorcery it is? Well, the answer is “Data Mining”.
So, are you listening to the term “Data mining” for the first time? But don’t be surprised if I tell you that you might have gone through this process a bazillion times since you started using the internet. Yup, that’s true, and a very fresh example is when Google prompts you to autosave your ID and passwords for future use.
Now, that you have a slight idea about data mining, let’s delve deeper into what it is. In this blog, you will learn-
-
What Is Web Data Mining?
-
What Is The Purpose Of Web Data Mining?
-
What Is The Process Of Web Data Mining?
What Is Web Data Mining?
Imagine bazillions of people using the internet, and new users adding up each day. All those users create their unique patterns when they do any sort of activity on the internet.
The advanced AI works by identifying and segregating our patterns and storing them in a cloud space called a data warehouse. This process of analysing, identifying, segregating and then storing them accordingly in the data warehouse is called Web Data mining.
Web data mining is a part of the process of analysis in the "knowledge discovery in databases" (KDD) is data mining.
Every day, heaps of data accumulate on the internet, and it is treated as gold because all the added data holds something useful for seeking users. But before the data can be stored, it is analysed, processed and made sure of its credibility.
What Is The Purpose Of Web Data Mining?
As mentioned earlier, every second of the day, loads of data are added to the internet. While browsing, the digital footprint added by each user becomes a valuable source of information, but if the information is just floating around in the cloud space without any precise arrangement, it becomes a hassle to access when needed. Web data mining helps organize it all, and it, in turn, helps work the AI better for you.
The process of data mining is fairly neutral in itself, but oftentimes, the credibility of the purpose is questionable, however, that doesn’t mean that nothing positive can come out of it. There are numerous ways in which Data mining helps us.
What Is The Process Of Web Data Mining?
Analysing the data given out daily by every internet user through their browsing history and several statistics and then identifying patterns simultaneously is a huge task. To sort it all out in a jiffy, AI automation and warehousing are used. It includes-
-
Data Collection
Web data mining means collecting data from all the available resources for a certain purpose. Mostly, web data mining is performed for the purpose of research. The data collection is an elaborate and extensive process. There are various questions which are put up to make a research a well-rounded and substantial one.
To answer the questions of all the “whats, whys, whos, whens, wheres and hows” of a research subject in detail, data collection is performed rigorously.
-
Data Cleaning
According to Wikipedia -
“Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record-set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.
Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting.”
-
Analysis
Data analysis is a key step in research, all the mined and cleansed data is inspected, and researchers go through the data thoroughly to find all the answers to the questions that are put up which will make the research complete. Various analysis techniques may be used to go through the data for drawing conclusions.
-
Interpretation
The conclusion drawn through analysing the data is the interpretation. Interpretation of data after analysis helps in the segregation and summarization of the outcome. It helps to categorize and store the outcome for future use.
The information is mined in 3 categories-
-
Web Content Mining
Web content consists of various types of data that include text, images, audio, video etc. that are added to the internet each day. All the content is classified as per their types and is mined accordingly. Text mining, image mining, audio mining and video mining are the classifications of web content mining.
2. Web Structure Mining
Web structure mining is the process of uncovering the underlying structure information from the web to form a webgraph.
Structure of a webgraph
A web graph consists of 2 parts:
-
Nodes (the web pages)
-
Hyperlinks connecting the nodes.
(Note: the connection between nodes can be one way or two-way)
Web structure mining is to get a structure summary of a website in general, the relationship between web pages linked by information or direct link connection. Web structure mining can be very useful to determine and judge the connection between two commercial websites and more.
3. Web Usage Mining
The digital footprints left by the users create unique data sets. These data sets reflect the user preference and behaviour of the users that help the AI to cater to them in a better way. Each small action is monitored and recorded (mined) so that when we visit any social platform in the future, we see feeds based on our previous actions.
Some Negative Aspects Of Web Data Mining
Privacy Is The Biggest Issue
Data mining by itself does not raise ethical concerns, but data breaches and unprotected data can. There have been numerous stolen data commercials over the years that have caused havoc in various parts of the world.
Intimate photos, credit reports, bank account login information, and other sensitive information were leaked, causing severe pain to users. People can lose their reputation while saving their lives and possibly even their peace of mind.
Data Mining And Monetization, Ethical Dilemma
Because big data allows us to understand better who we are and what we want, the bigger question is whether it is ethical to monetize sensitive data. By gaining access to personal records and misusing people for their gain, the lines between what is acceptable and what is not can be blurred.
Many companies use information such as medical records, location tracking, and even search history to persuade users to buy products that are clinically unproven, unnecessary, or inexpensive.
Security At Risk
Mined Data sold on the black market frequently poses a security risk to the public. This information can be used for fraudulent activity, reputational harm, private information theft, and other purposes. In 2015, there was a data breach in Washington that exposed the fingerprints of millions of people. This is a serious threat because many banks can only approve transactions and change passwords using matching fingerprints. Hackers attempt to steal information, but mining data from cyberspace and selling it to hackers is riskier.
What is the veracity of the data mining output? It all depends on what kind of details you deliver online.
E.g., a person who wishes to create a buzz about themselves and posts information that is a hoax and has been done only to gain publicity. Data miners may mine incorrect data if they use manipulative information. This situation can be exploited by the individual in question to gain access to uncalled favours and other benefits. The same is true of political beliefs. According to an Oxford Internet Institute study, people in about nine countries use social media platforms to spread false information. When data miners obtain this information, it becomes a fact, resulting in fake news.