This literature review investigates the use of web crawling techniques for analyzing web trends and improving business productivity. It examines various approaches, including machine learning algorithms for modeling web traffic, big data analytics for forecasting tourism destination arrivals, and the PolarHub web crawling engine for geospatial data discovery. The review discusses the advantages and limitations of each technique, highlighting their applicability in different contexts. Key themes include identifying bot traffic, analyzing social media data, and leveraging web search queries for business insights. The reviewed solutions aim to address challenges such as non-stationary web traffic, overcrowded tourist destinations, and the efficient retrieval of geospatial resources, ultimately contributing to increased business efficiency and informed decision-making. Desklib offers a platform for students to access this document and numerous other solved assignments and study resources.