Web Usage Mining involves applying data mining techniques to discover usage patterns from web data to better understand and serve web-based applications. The process consists of three phases: preprocessing, pattern discovery, and pattern analysis. This paper provides a detailed taxonomy of web usage mining research, including academic and commercial efforts. It also describes the WebSIFT system as a prototype for web usage mining. Web usage mining can be applied to various data sources, including server logs, client-side data, and proxy-level data. Server logs are a key source for tracking user behavior, but they may not always be reliable due to caching and other factors. Client-side data collection, such as through JavaScript or Java applets, can provide more accurate tracking but may face challenges in user cooperation. Proxy-level data can also be used to analyze browsing behavior across multiple sites. The paper discusses various data abstractions used in web usage mining, including users, server sessions, episodes, clickstreams, and page views. It also covers the challenges involved in preprocessing data, such as identifying users and server sessions, and inferring cached page references. Pattern discovery involves techniques like statistical analysis, association rules, clustering, classification, sequential patterns, and dependency modeling. These methods help in identifying user behavior, preferences, and trends. Pattern analysis then filters out uninteresting patterns and provides insights for applications such as personalization, system improvement, site modification, business intelligence, and usage characterization. The paper also addresses privacy concerns related to web usage mining, emphasizing the need for guidelines and regulations to protect user anonymity while allowing for useful analysis. It highlights the importance of balancing data collection with user privacy and the role of frameworks like P3P in managing privacy policies. In conclusion, web usage mining is a rapidly growing field with significant potential for improving web-based applications. However, it raises important scientific and ethical questions that need to be addressed to ensure responsible and effective use of the technology.Web Usage Mining involves applying data mining techniques to discover usage patterns from web data to better understand and serve web-based applications. The process consists of three phases: preprocessing, pattern discovery, and pattern analysis. This paper provides a detailed taxonomy of web usage mining research, including academic and commercial efforts. It also describes the WebSIFT system as a prototype for web usage mining. Web usage mining can be applied to various data sources, including server logs, client-side data, and proxy-level data. Server logs are a key source for tracking user behavior, but they may not always be reliable due to caching and other factors. Client-side data collection, such as through JavaScript or Java applets, can provide more accurate tracking but may face challenges in user cooperation. Proxy-level data can also be used to analyze browsing behavior across multiple sites. The paper discusses various data abstractions used in web usage mining, including users, server sessions, episodes, clickstreams, and page views. It also covers the challenges involved in preprocessing data, such as identifying users and server sessions, and inferring cached page references. Pattern discovery involves techniques like statistical analysis, association rules, clustering, classification, sequential patterns, and dependency modeling. These methods help in identifying user behavior, preferences, and trends. Pattern analysis then filters out uninteresting patterns and provides insights for applications such as personalization, system improvement, site modification, business intelligence, and usage characterization. The paper also addresses privacy concerns related to web usage mining, emphasizing the need for guidelines and regulations to protect user anonymity while allowing for useful analysis. It highlights the importance of balancing data collection with user privacy and the role of frameworks like P3P in managing privacy policies. In conclusion, web usage mining is a rapidly growing field with significant potential for improving web-based applications. However, it raises important scientific and ethical questions that need to be addressed to ensure responsible and effective use of the technology.