Many systems, from computing systems, physical systems, business systems, to social systems, are only observable indirectly from the events they emit. Events can be defined as real-world occurrences and they typically involve changes of system states. Events are naturally temporal and are often stored as logs, e.g., business transaction logs, stock trading logs, sensor logs, computer system logs, HTTP requests, database queries, network traffic data, etc. These events capture system states and activities over time. For effective system management, a system needs to automatically monitor, characterize, and understand its behavior and dynamics, mine events to uncover useful patterns, and acquire the needed knowledge from historical log/event data.

Event mining is a series of techniques for automatically and efficiently extracting valuable knowledge from historical event/log data and plays an important role in system management. The purpose of this tutorial is to present a variety of event mining approaches and applications with a focus on computing system management. It is mainly intended for researchers, practitioners, and graduate students who are interested in learning about the state of the art in event mining.

Link to tutorial: https://users.cs.fiu.edu/~taoli/event-mining/