steplabeled

Steplabeled is a labeling approach in which annotations are attached to discrete steps within a sequence or process, rather than to individual time points or objects. Each step is assigned a label that describes its category, action, or status, and the ordering of steps is preserved.

In steplabeled datasets, the unit of annotation is the step, and the label taxonomy defines the set of permissible categories for steps. Defining clear step boundaries and a consistent label schema is essential to ensure comparability across sequences and annotators. The approach emphasizes stage-level information, which can simplify analysis when the order of actions matters more than precise timing.

Common applications include process mining and workflow analytics, instructional design, and activity recognition in robotics or manufacturing. Steplabeled data supports interpretability by aligning labels with human-understandable stages such as plan, prepare, perform, and verify, or other domain-specific steps. It is often used to model procedural knowledge, standard operating procedures, or sequential tasks.

Advantages of steplabeled data include easier auditability, improved alignment with domain expertise, and resilience to temporal jitter in the data. Challenges involve determining where one step ends and the next begins, selecting an appropriate level of granularity, and handling cases where steps are skipped, overlapped, or simultaneous. Effective steplabeled datasets typically rely on clear annotation guidelines and consistency checks to maintain label quality across sequences.

Steplabeled is related to, but distinct from, frame-level labeling, event segmentation, and sequence tagging. It is frequently used in conjunction with timeline data, process models, or hierarchical annotations to capture both the sequence of steps and finer-grained details when needed.