Sequence Synopsis: Optimize Visual Summary of Temporal Event Data

Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.

1 Introduction

Event sequence data, i.e., multiple series of timestamped or ordered events, is increasingly common in a wide range of domains. Website click streams, user interaction logs in software applications, electronic health records (EHRs) in medical care and vehicle error logs in automotive industry can all be modeled as event sequences. It is crucial to reason about and derive insights from such data for effective decision making in these domains. For example, by analyzing vehicle error logs, typical fault development paths can be identified, which can inform better strategies to prevent the faults from occurring or alert drivers in advance, and therefore improve driver experience and reduce warranty cost. Similarly, by analyzing users' interaction log with software applications, usability issues and user behavior patterns can be identified to inform better designs of the interface.