Data mining of big data, also known as Big Data Mining (BDM), is essentially applying established data analysis methods to extremely large volumes of information to uncover valuable insights.
Defining Big Data Mining (BDM)
Based on the reference, Big Data Mining (BDM) is an approach that uses the cumulative data mining or extraction techniques on large datasets / volumes of data. This means it takes the familiar processes of finding patterns, trends, and anomalies in data and scales them up to handle the immense size and complexity characteristic of "Big Data."
It is distinct from traditional data mining primarily due to the sheer immense volume of data involved, which requires specialized tools and techniques to process efficiently.
Core Objectives: Patterns and Value Extraction
The primary focus of BDM is clear: retrieving relevant and demanded information (or patterns). Organizations don't just collect vast amounts of data for its own sake; they do so to find hidden knowledge within it.
BDM aims at extracting value hidden in data of an immense volume. This value can manifest in various forms, such as:
- Identifying customer segments for targeted marketing.
- Predicting market shifts or consumer behavior.
- Detecting fraudulent activities.
- Optimizing business processes.
- Gaining scientific or medical breakthroughs.
Key Aspects and Focus
When discussing BDM, several core aspects stand out:
- Technique Application: It relies on existing data mining or extraction techniques (like classification, clustering, regression, association rule mining) but adapted for scale.
- Data Scale: It specifically targets large datasets / volumes, which is the defining characteristic differentiating it from traditional data mining on smaller or more manageable datasets.
- Information Discovery: A key goal is retrieving relevant and demanded information – finding what is useful and actionable within the noise.
- Pattern Identification: It heavily focuses on uncovering patterns that are not immediately obvious through simple analysis.
- Value Extraction: The ultimate aim is extracting value, translating the discovered patterns and information into tangible benefits.
Why BDM Matters in the Age of Big Data
The proliferation of data from various sources (sensors, social media, transactions, logs, etc.) has created "Big Data" environments where traditional data mining tools and methods often fail due to volume, velocity, and variety constraints. BDM provides the necessary frameworks and algorithms to handle these challenges, making it possible to derive meaningful insights from these massive datasets and gain competitive advantages or make better decisions.
Practical Insights (Simple Examples)
BDM is applied across numerous industries. Some simple examples include:
- Analyzing billions of social media posts to gauge public sentiment about a product.
- Processing years of customer transaction logs from a large retailer to identify complex purchasing patterns.
- Mining vast amounts of sensor data from machinery to predict maintenance needs before failures occur.
- Analyzing web server logs for a major website to understand user navigation paths and optimize site layout.
In essence, BDM is the process of digging through mountains of data to find the valuable gold hidden within – the patterns and insights that drive understanding and action.
