Understanding user interactions in digital systems is essential in analyzing user behaviors and improving system usability. However, a collection of interaction sequences is often large and unstructured, making it challenging to uncover interaction patterns. To address this challenge, we introduce a visual analytics approach that integrates hierarchical clustering and process mining techniques to support analysts in exploring unstructured, large interaction sequence data. Our system employs a tailored dynamic time warping-based similarity measure to enable comparison of interaction sequences. Based on the sequence similarities, we provide stepwise, interactive navigation of clustering results with contextual visual cues for refinement and validation. We further apply process mining to characterize derived clusters. Through these hierarchical clustering and process mining steps, analysts can progressively uncover meaningful interaction patterns while utilizing visual guidance and incorporating domain expertise. We demonstrate our system's effectiveness and applicability through two case studies involving system designers, developers, and domain experts.

We present a visual analytics approach for multi-level visual exploration of users' interaction strategies in an interactive digital environment. The use of interactive touchscreen exhibits in informal learning environments, such as museums and science centers, often incorporate frameworks that classify learning processes, such as Bloom's taxonomy, to achieve better user engagement and knowledge transfer. To analyze user behavior within these digital environments, interaction logs are recorded to capture diverse exploration strategies. However, analysis of such logs is challenging, especially in terms of coupling interactions and cognitive learning processes, and existing work within learning and educational contexts remains limited. To address these gaps, we develop a visual analytics approach for analyzing interaction logs that supports exploration at the individual user level and multi-user comparison. The approach utilizes algorithmic methods to identify similarities in users' interactions and reveal their exploration strategies. We motivate and illustrate our approach through an application scenario, using event sequences derived from interaction log data in an experimental study conducted with science center visitors from diverse backgrounds and demographics. The study involves 14 users completing tasks of increasing complexity, designed to stimulate different levels of cognitive learning processes. We implement our approach in an interactive visual analytics prototype system, named VISID, and together with domain experts, discover a set of task-solving exploration strategies, such as "cascading" and "nested-loop", which reflect different levels of learning processes from Bloom's taxonomy. Finally, we discuss the generalizability and scalability of the presented system and the need for further research with data acquired in the wild.

Real-world event sequence data, such as activity logs, eye-tracking data, simulation data, and electronic health records, often share characteristics such as a large alphabet of events, fragmentation, noise, and high complexity which makes them difficult to analyze in their raw form. Because of this, simplification and preprocessing through various data transformations are commonly required before the data can be effectively visualized and analyzed. Existing methods for such data transformation are either manually applied and rely heavily on user expertise, or use algorithmic approaches to apply bulk operations which can imply the loss of potentially important information without users being aware. To bridge this gap, we propose a visual analytics approach that aims to successively increase the quality of noisy event sequences by supporting an interactive, context-aware application of data transformations. This is achieved by providing cues concerning the potential loss of information that transformation operations may imply and allowing users to explore, and visually assess their impact on the data. Therefore, a central feature of the approach is that users can tune the data transformation process so that important identified data characteristics are preserved. We motivate the proposed approach in the domain of air traffic control and illustrate it through a usage example, using event sequences derived by merging eye-tracking and simulator data from a human-in-the-loop simulation experiment with 14 air traffic controllers.