capture interaction traces

Debugging embedded programs is notoriously challenging due to tight software-hardware coupling, and existing tools often rely on external hardware probes or serial logging, resulting in low efficiency. This work proposes Inline, a novel programming tool that, for the first time, enables real-time inline visualization of hardware logs directly within source code. It introduces a domain-specific expression language to support programmable manipulation of logs, allowing developers to intuitively trace execution flow and precisely localize faults. Seamlessly integrated into standard embedded development environments, Inline significantly lowers the barrier to effective debugging. A user study with twelve participants demonstrates marked improvements in both debugging efficiency and accuracy when using the tool.

Existing evaluation approaches for streaming process mining algorithms predominantly rely on static logs or synthetic event streams, which fail to capture the complexity of real-world event streams in IoT environments—such as out-of-order events, concurrency, incomplete cases, and concept drift. This work addresses this gap by introducing, for the first time, a feature framework from data stream research into streaming process mining. It proposes an intent-oriented event stream generation methodology, extends the conceptual model of event streams, and implements a prototype tool, Stream of Intent. This tool enables customizable configuration of key stream characteristics reflective of real-world scenarios, facilitating the generation of controlled, reproducible, and realistically complex event streams. Consequently, it significantly enhances the relevance and adaptability of algorithm evaluation and development in streaming process mining.

Current AI creativity support tools generate low-level interaction logs—such as clicks and parameter adjustments—that poorly capture users’ creative intent, limiting agents’ understanding of the design process. This work proposes a novel approach that transforms raw, noisy logs into structured, high-level behavioral workflow graphs by abstracting semantic action tokens like MODIFY_Prompt and GENERATE_Image. For the first time, this method enables a meaningful mapping from low-level interactions to interpretable creative workflows. Through log parsing, behavioral abstraction, and sequence modeling, it produces a structured representation amenable to downstream mining and probabilistic reasoning. This representation lays the foundation for “process-aware agents” capable of offering design suggestions or explaining decisions grounded in users’ historical creative behavior.

This paper addresses the fragmentation between wearable-device data and process mining. We propose an event log enhancement method for personal behavior modeling, integrating smartwatch data (sleep, heart rate, physical activity) and digital calendar data into process event logs via temporal alignment, multi-source data aggregation, and event derivation. Novel semantic events—such as “deep sleep onset” and “prolonged sitting before meeting”—are introduced at the event, case, and activity levels. Three wearable-data fusion pathways are innovatively designed, relaxing process mining’s traditional reliance on structured business logs. Evaluated on 30 days of real-world, multimodal data from individual users, our approach significantly improves the granularity and interpretability of behavioral pattern discovery. It establishes a scalable, process-mining-based paradigm for personalized productivity optimization and holistic well-being analysis.

Existing work primarily focuses on action-set extraction, neglecting end-to-end routine model discovery and lacking validation on real-world UI logs corrupted by execution variability and human errors. This paper proposes a noise-tolerant clustering method that, for the first time, directly enables complete and high-precision extraction of routine logs from raw UI interaction traces—thereby facilitating routine pattern discovery in robotic process automation (RPA). Our approach integrates behavioral similarity measurement with an adaptive noise-filtering mechanism to robustly identify and reconstruct routine execution paths. Extensive experiments across nine publicly available UI log datasets demonstrate that our method achieves an average F1-score improvement of over 15% under high-noise conditions, significantly outperforming state-of-the-art techniques. The key contributions include: (i) the first end-to-end routine discovery framework tailored for noisy UI logs; (ii) a principled noise-resilient clustering strategy grounded in behavioral semantics; and (iii) empirically validated superiority in both accuracy and robustness.

This study addresses the challenge of generating structured event logs from multimodal data such as videos to support business process mining. The authors propose an end-to-end approach that first maps video frames into feature vectors using image embeddings, then performs temporal segmentation via an inter-frame similarity matrix. Subsequently, a generalized few-shot classification method automatically assigns semantic labels to the resulting segments, yielding a timestamped, structured event sequence. This work represents the first integration of image embeddings with few-shot learning for the automatic transformation of raw video into process-mining-ready event logs, thereby overcoming the traditional reliance on pre-structured input data. The method’s effectiveness and practicality are validated through experiments in real-world scenarios.

Electronic health record (EHR) audit logs contain rich, multidimensional information about clinical activities, yet lack a unified modeling framework. This work proposes a “multi-axis trace” perspective that simultaneously associates each logged action with clinician behavior, patient care trajectories, team collaboration patterns, and repetitive workflow structures, thereby uncovering its multifaceted clinical semantics. Building on this insight, we develop a representation learning framework that preserves the multi-axis structure by pretraining a foundation model directly on raw audit log streams to learn general-purpose representations. The resulting approach establishes a unified data representation and evaluation paradigm applicable to diverse downstream tasks, including clinical workload analysis, patient outcome prediction, team coordination assessment, and workflow modeling.

Existing debugging tools excel at verifying hypotheses but struggle to support hypothesis generation, as programmers must manually reconstruct the program’s state evolution. This work proposes a novel debugging paradigm centered on complete execution traces, leveraging program tracing techniques to record and temporally visualize the actual code paths executed, rather than relying on the static structure of the source code. By presenting runtime behavior in a chronological and contextualized manner, this approach significantly enhances the comprehensibility of program execution, thereby facilitating more efficient hypothesis generation during debugging. We implement a prototype system and conduct preliminary experiments that demonstrate its effectiveness in improving program understanding efficiency, while also uncovering key challenges and promising directions for future research.

This work addresses the challenge of effectively integrating Internet of Things (IoT) data with business process event logs, which stems from their heterogeneous origins and differing levels of abstraction. Direct incorporation of raw IoT data often leads to excessive log complexity, thereby hindering process analysis. To overcome this, the paper proposes a structured fusion approach within the Object-Centric Event Log (OCEL) framework, introducing a mapping and integration mechanism that seamlessly embeds process-relevant IoT data into standard OCEL logs without requiring domain-specific schemas. The resulting IoT-enhanced event logs, generated by an extensible tool, are fully compatible with mainstream process mining tools. Empirical validation in real-world scenarios demonstrates the method’s effectiveness in enabling downstream process analysis and visualization.