Big Data Mining

What is Big Data Mining?

Success in today’s data-driven world lies in the ability to utilize Big Data Mining. Data Mining is a complex process of calculating a large chunk of data sets to uncover information like hidden patterns, unknown correlations, latest market trends and customer preferences. With the right software, data mining can help organizations to make business decisions.

When doing Data Mining, one of the most important goals is to analyse huge quantities of data in order to uncover hidden patterns and connections in the data. The goal of our Big Data Mining service is to uncover new insights or patterns, as well as to create predictions about what will happen in the future.


Big Data in Cyber Security

Data mining has also proven to be a useful tool in cyber security solutions, allowing for the discovery of vulnerabilities and the collection of indicators for baselining

  • To Know Your Industry: There is an avalanche of information available about your industry that you may be interested in learning about and Big Data Mining techniques can help you extract and make sense of such information
  • To Know Your Competitors: Finding potential growth opportunities can be managed to accomplish by analyzing consumer feedback and recommendations, as well as information gleaned from competing goods and services.
  • To Know Your Customers: In the world of cyber security, with the help of big data approaches, you may be able to gain important information into your clients' purchase habits, requests, and recommendations.
the need

Our Services

Web Data Mining

We extract data from the web to meet your business needs, private or public. We help your research team mine web data.

Social Media Mining

It is a method for finding hidden patterns and trends in social media platforms like Twitter, LinkedIn, Facebook, and others.

SQL Data Mining

CryptoMize's data mining experts can create large databases of information for modelling, perfect for businesses.

Word Data Mining

This extracts significant insights from any data. It collects nonsensical data and transforms it into useful information.

Web Data Mining

With the rapid growth of the Web, there has been a massive amount of data generated from Web activities. Web data mining is the practice of analyzing web-based big data to extract meaningful information, knowledge and insights. In its most basic form, the goal of web mining is to extract valuable information from the World Wide Web and from the patterns of its users' behaviour.

In addition to categorising online documents and identifying web pages, web mining contributes to the improvement of the power of web search engines.It is utilized for Web Searching, such as Google, Yahoo, and others, as well as Vertical Searching, such as FatLens, Become, and others. It is used to forecast user behaviour by analysing web traffic. Web mining is extremely beneficial to a specific Website and e-service, for example, landing page optimization. Web data mining consists of three different types of techniques of mining:

Web Content Mining

Extracting useful information from the content of the web documents– text, image, audio, video etc.

Web Structure Mining

Discovering structure data from the web- nodes, and hyperlinks as edges connecting related pages.

Web Usage Mining

Identifying or discovering interesting usage patterns from large data sets to understand user behaviors.


SQL Data Mining

  • Data warehousing with SQL Server is something that many people are interested in.
  • SQL Server provides a platform for data mining, which involves making predictions about the data.
  • A few tasks are used to solve business issues. Sort, Cluster, Predict, Sequence and Associate are the tasks.
  • Data mining algorithms in SQL Server can solve the aforementioned business problems.
  • Our data mining experts can create large databases of data for modelling, which is ideal for businesses of all sizes.

Social Media Data Mining

Using the Social Media Data Mining services provided by CryptoMize, you can ensure that you receive vital information relevant to your business interests by filtering through billions of posts, interactions, user profiles, metadata, and other data in order to analyse trends and keep you up to date with the latest developments.

  • Extraction: In addition to text, images, and videos, our data mining professionals are capable of extracting massive amounts of information from a variety of social media platforms.
  • Validation: Our experts eliminate redundancies, abnormalities, and unnecessary items from the database while regularly cleaning and updating the remaining data.
  • Preparation: Our skilled experts correctly organise and arrange the database into client-specific tables and fields to facilitate information retrieval, transfer, and dissemination.
  • Reporting: As one of the top online data mining businesses, we provide mined data in a user-friendly manner. Also, important data is displayed in a way that is business-oriented and suitable with all devices and platforms.

Word Data Mining

Word mining is a process of getting useful sets of data from any text, including published documents and non-public data. It is the first step to efficiently find useful information. Word data mining is an innovative, state-of-the-art software solution that extract keywords from web pages, extract synonyms (and related terms) of each keyword, extract domain names (website addresses) of the related domains of the extracted keywords and regenerate positions of the extracted keywords in order to use them as seed for SEO backlinks .

Our certified experts are capable of extracting and analysing information from large quantities of text stored in databases, printed materials, or in Microsoft Word documents. Financial transaction data, legal problems, medical and scientific research are just a few of the sectors to which we have provided our services in the past.

What are the benefits of Big Data?

In order to do sophisticated analytics, a Big Data solution architecture is required, which allows for the evaluation of both current and historical data from a variety of data sources.


A data breach may be detected quickly by using data mining and security system data to analyse and predict risk patterns.


Big data analytics may assist in monitoring risks by tracking many system events. This technique may stop data leaks.

Intrusion Detection

While real-time monitoring and vulnerability hunting is difficult, big data analytics may assist by automating the process.


Security insights are critical for maintaining effective cyber defense, for which analytics and reporting may assist us with.


What We Are Aiming For?

Our solutions utilize advanced analytical methods, cutting-edge technologies, and top industry practices to explore the hidden patterns in your data, to transform big data into actionable insights.

Our data mining services are available to everyone interested in improving their business performance. We will create a unique solution just for you based on your specific needs. With us, you won't have to worry about your project deadlines, as we offer flexible payment terms.



Frequently Asked Questions

Big data mining has emerged in the last few years as a means to harvest, process, and analyze huge amounts of data. While many techniques have existed for the past two decades to mine data sets, the volume of data being mined is growing exponentially. As a result, these methods are no longer sufficient to ensure high-quality results. Big Data mining refers to the use of advanced machine learning algorithms to search for patterns in massive data sets. The enormous volume of data available today has created an opportunity for organizations to harness the power of their own data resources in order to gain insights that can be used for decision support, customer support, product innovation and business optimization.

Data mining is the process of examining and analysing vast amounts of data in order to uncover patterns in big data. The methods were derived from statistics and artificial intelligence (AI), with a dash of database management tossed in for good measure. Data mining usually has one of two goals: classification or prediction. Classification is the act of sorting data into groups or 'classes' that share some common attribute. For example, in marketing, companies use data mining to automatically assign households to demographic groups such as 'soccer moms', 'dinner-party couples' or 'blue-collar dads'. This allows them to personalise their sales pitches in order to appeal more directly to each group. Classification can also be used for security, fraud detection and medical diagnosis.
Prediction is the process of extrapolating from past observations to make a reasonable guess about what the future holds. Data miners are trying to predict things like customer behaviour, disease outbreaks or credit risk. They'll take a set of information -- called a dataset -- and look for patterns that suggest what might happen in the future. A data miner will look at millions of records of people's buying habits, for example, and try to find clusters of customers who have similar tastes. Then she will make a prediction about the kinds of products they are likely to buy together.

The kinds of Big Data that can be mined are:
Flat Files
Relational Databases
Transactional Databases
Multimedia Databases
Spatial Databases
Time Series Databases
World Wide Web(WWW)

The areas where Data Mining is widely used are as follows: Financial Data Analysis: Financial data in the banking and financial industries is generally credible and of high quality that requires systematic data analysis and data mining. Retail Industry: In the retail industry, data mining aids in the identification of client buying patterns and trends, resulting in improved customer service and increased customer retention and satisfaction. Telecommunication Industry: Data mining in the telecommunications industry aids in the detection of telecommunication patterns, the detection of fraudulent actions that promotes better utilisation of resources, and the improvement of service quality. Biological Data Analysis: Bioinformatics includes biological data mining as a significant component. Data mining contributes in analysis of genomic networks and protein pathways, the discovery of structural patterns and so on. Intrusion Detection: It is used for developing data mining algorithms for intrusion detection and to construct discriminating qualities, use association and correlation analysis, as well as aggregation.

There are various procedures involved in data mining. The procedures are as follows: Data Preparation: The cleansing of a huge volume of data to get the values to an amendable form is referred to as data preparation. It is accomplished by employing statistical approaches to derive simple profits from complex ones. Model Building: It has to do with selecting a specific model for calculating performance. It entails a variety of strategies in order to get the best results from the data mining process. Composition: This is the final stage that allows you to select the prior model to apply to your new data in order to achieve the desired results.

The 5 data mining techniques are:

Analysis:This method is used to find vital and relevant data and metadata. It's used to divide data into separate categories.

Association: It refers to a technique for identifying meaningful relationships (dependency modelling) among various variables in massive databases. This technique can assist you in uncovering hidden patterns in the data that can be used to discover variables within the data as well as the co-occurrence of many variables that appear frequently in the dataset.

Anomaly:This is the observation of data objects in a dataset that do not follow a predictable pattern or behave in a predictable manner. Outliers, novelties, noise, deviations, and exceptions are all terms used to describe anomalies. They frequently provide crucial and useful data.

Clustering: Clustering analysis is the process of identifying groups and clusters in data so that the degree of association between two objects is highest if they belong to the same group and lowest if they do not. Customer profiles can be created as a result of this analysis.

Regression: The technique of discovering and analysing the relationship between variables is known as regression analysis. If one of the independent variables is changed, it can help you comprehend how the characteristic value of the dependent variable changes.

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