Author: Swetha D and Hanush Shandilya R
Published On: 2026-08-08
The rapid growth of web-based applications has resulted in the generation of massive volumes of web log data, providing valuable insights into user navigation patterns and website performance. Efficient analysis of these logs enables organizations to understand user behavior, optimize web services, and improve decision-making. This study presents a Hadoop MapReduce-based framework for trend analysis of web log files to process large-scale, heterogeneous log data efficiently. The proposed approach includes data collection, preprocessing, feature extraction, distributed storage using the Hadoop Distributed File System (HDFS), and parallel processing through the MapReduce programming model. Pattern analysis techniques are employed to identify user access trends, navigation behavior, and frequently visited resources. The framework supports scalable and fault-tolerant processing while reducing execution time for large datasets. Experimental observations demonstrate that the proposed methodology effectively extracts meaningful usage patterns, providing actionable insights that can assist website administrators in enhancing content organization, improving user engagement, and optimizing overall website performance. The proposed framework offers a reliable and scalable solution for web usage mining in big data environments.
Keywords - Web Log Analysis, Hadoop, MapReduce, Hadoop Distributed File
System (HDFS), Web Usage Mining, User Behavior Analytics, Trend Analysis, Big Data Processing, Distributed Computing.
9
2026
1
Research Article
2/11, SASTRI NAGAR, KOYEMBEDU, CHENNAI-600107
9488577176
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