Unlocking Hidden Patterns: Algorithmic Strategies for Preprocessing Web Usage Data by Dr. Arti Pandey is a comprehensive exploration of web usage mining (WUM), a vital subfield of data mining that transforms raw, noisy web server logs into actionable insights on user behavior, enabling enhanced website designs, personalized experiences, and informed business decisions. Drawing from foundational concepts in data mining, artificial intelligence, and information retrieval, the book delves into the critical preprocessing phase-encompassing data cleaning, user identification, session recognition, path completion, and transaction identification-to address challenges like scalability, heterogeneous data, and distributed systems. It introduces innovative algorithms such as the Time-Based Constrained Sequential Mining Algorithm (TBCSM) and the Single Scan Pattern Recognition Algorithm (SSPRA), evaluating their efficiency against established methods like Apriori and FP-Growth for handling large-scale web logs and uncovering sequential patterns. Structured from introductory concepts to advanced strategies and future directions, this work pays tribute to web mining pioneers while equipping researchers, students, and practitioners with practical tools to navigate the complexities of web data, ultimately fostering intelligent, user-centric digital systems in an ever-evolving online landscape.