Identifying Frequent User Tasks from Application Logs

Abstract

In the light of continuous growth in log analytics, application logs remain a valuable source to understand and analyze patterns in user behavior. Today, almost every major software company employs analysts to reveal user insights from log data. To understand the tasks and challenges of the analysts, we conducted a background study with a group of analysts from a major software company. A fundamental analytics objective that we recognized through this study involves identifying frequent user tasks from application logs. More specifically, analysts are interested in identifying operation groups that represent meaningful tasks performed by many users inside applications. This is challenging, primarily because of the nature of modern application logs, which are long, noisy and consist of events from high-cardinality set. In this paper, we address these challenges to design a novel frequent pattern ranking technique that extracts frequent user tasks from application logs. Our experimental study shows that our proposed technique significantly outperforms state of the art for real-world data.