Types of Problem Suited to Big Data Analysis(1)

Bilal Hussain Malik

21/3/25

                                          Types of Problem Suited to Big Data Analysis

                                                  Pattern Recognition in Massive Datasets

Big data analytics revolutionizes pattern recognition by processing vast, complex datasets that traditional methods cannot handle. In financial sectors, banks employ big data technologies to detect fraudulent transactions by analyzing millions of operations in real time. Machine learning algorithms scrutinize spending patterns, flagging anomalies such as unusual transaction locations or amounts, which might indicate fraud. Without big data tools like Hadoop for storage and Spark for real-time processing, identifying these subtle, global patterns would be impractical. Similarly, in healthcare, big data aggregates diverse sources—electronic health records, wearable device outputs, and genomic datato predict and track disease outbreaks.

 For instance, analyzing search query trends and hospital admission reports enables early detection of flu outbreaks, allowing for timely public health interventions. These applications demand not only high-volume data processing but also the ability to handle high-velocity data streams while ensuring accuracy. Machine learning plays a pivotal role by training models on historical data to predict risks, whether financial or medical. Apache Spark further enhances these capabilities by enabling real-time data stream processing, ensuring that insights are both immediate and actionable. Together, these technologies transform raw data into actionable intelligence, proving indispensable in sectors where precision and speed are critical. The ability to uncover hidden patterns at scale underscores big data's value in modern analytics, driving innovations in fraud prevention, healthcare, and beyond.