Data Extraction Approach for Aggregator Platforms

Abstract

The digital landscape is rapidly evolving, and aggregator platforms have become crucial intermediaries in sectors such as e-commerce, food delivery, news, job portals, real estate, and education technology (EdTech). These platforms collect and consolidate data from multiple sources, providing users with a unified, accessible interface. However, the efficiency and accuracy of data extraction are critical challenges due to the dynamic nature of web content and the variability in data structures across different websites.
Traditional data extraction methods employed by aggregator platforms often rely on manual processes or basic automation tools, which are not sufficient to handle the complexity and variability of modern web data. These methods can result in significant inefficiencies, such as delays in data aggregation, inaccuracies, and incomplete datasets. This, in turn, affects the reliability and performance of aggregator platforms, leading to potential business risks.