Mining software usage data
Mohammad El‐Ramly · 2004
Many software systems collect or can be instrumented to collect data about how users use them, i.e., system-user interaction data. Such data can be of great value for program understanding and reengineering purposes. We demonstrate that sequential data mining methods can be applied to discover interesting patterns of user activities from system-user interaction traces. In particular, we developed a process for discovering a special type of sequential patterns, called interaction patterns. These are sequences of events with noise, in the form of spurious events that may occur anywhere in a pattern instance. In our case studies, we applied interaction pattern mining to systems with considerable different forms of interaction: Web-based systems and legacy systems. We used the discovered patterns for user interface reengineering, and personalization. The method is promising and generalizable to other systems with different forms of interaction.