Understanding how resources contribute to business process execution is essential for identifying ineficiencies and improving operational performance. However, existing tools often ofer fragmented support for resource analysis and lack integrated, interpretable visualizations. This paper introduces a visual resource analytics tool that supports resource-centric analysis across four key dimensions: resource allocation, performance, workload distribution, and capacity utilization. The tool enables practitioners to explore resource behavior through interactive visualizations, comparative metric views, and contextual overlays on process models. By making resource behavior more transparent and analyzable, the tool addresses a critical gap in process mining practice.
Process mining has emerged as a key technology for analyzing and improving business processes based
on event data [
Existing process mining tools typically ofer only fragmented support for resource analysis, often
limited to isolated performance reports, simple utilization charts, or static dashboards that present
basic counts or averages. These views frequently focus on single metrics in isolation, such as case
completion times or activity throughput, without enabling joint exploration of how diferent resource
dimensions interact. Moreover, most tools lack integrated support for visually connecting resource
performance back to process structure, leaving analysts to manually piece together information from
multiple screens or reports. Visualizations, when provided, are often treated as secondary by-products of
algorithmic implementations, rather than being systematically designed for analytical clarity, cognitive
interpretability, or decision support [
As process data grows in scale and complexity, there is an increasing need for user-centric interfaces
that not only support flexible exploration but also enable focused and task-oriented analysis. Recent
research in human-centered visual analytics emphasizes the importance of context-sensitive interaction,
dynamic filtering, and cognitively efective visual design to improve task accuracy and reduce cognitive
load [
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To address this gap, we present a visual analytics tool that supports exploratory, multi-dimensional
analysis of resource behavior in business processes. The tool integrates four key dimensions of
resource analysis: allocation, performance, workload distribution, and capacity utilization into a unified,
interactive interface. Designed with cognitive usability principles in mind [
This demonstration builds upon our earlier work, which introduced the algorithmic approach and
initial prototype for resource behavior analysis [
The resource analysis tool introduces a novel technique which addresses key gaps in existing process mining tools by ofering a comprehensive, interactive, and visually grounded approach to resource analysis. The following innovations define its core contributions:
Multi-Metric Integration. Traditional process mining tools often focus on isolated behavioral
metrics, such as allocation or performance, ofering limited support for integrated analysis across
multiple dimensions[
Visualizing workload shares supports fairness assessment and organizational eficiency. • Capacity utilization evaluates how resource workload aligns with estimated capacity, embedded in the process flow. Sustained high utilization may reveal bottlenecks and resource strain, while chronic underutilization could indicate idle time, process waiting, or stafing misalignments. This perspective enables proactive capacity balancing and workload optimization.
By integrating these four metrics, our tool enables analysts to examine how resources behave, how eficiently they perform tasks, how workload is distributed, and how efectively capacity is utilized across a process.
Visual-First Design. While process mining literature has traditionally treated visualization as a
secondary output of algorithmic analysis [
Figure 1 illustrates the main interface of the tool, showcasing how multiple resource dimensions are visually represented and interactively explored through integrated views and contextual process model overlays.
Process Model Enhancement. Resource data is embedded directly into the process model by overlaying the metrics onto Directly-Follows Graphs (DFGs). This integration allows analysts to assess resource behavior within the process context, linking capacity or performance issues directly to specific activities and execution paths. This contextual embedding supports localized diagnosis of bottlenecks and ineficiencies.
User-Centric Interaction. Designed for process analysts, the tool is built with a strong focus on usability and analytical flexibility by providing a responsive and intuitive interaction layer. Users can conduct side-by-side comparisons of diferent metrics (e.g., resource performance vs. workload distribution), leverage hover-based tooltips to access detailed metric values, and apply filters to focus on specific resources, roles, or activities. The DFG view supports zooming and panning for seamless navigation of complex workflows. These interactive features enable both high-level overviews and granular investigations, positioning the tool as a robust platform for resource analysis in process mining. Together, these features position the tool as a robust and user-friendly analytics platform for resource analysis in process mining, addressing both current limitations in tool support and the increasing need for actionable, visually communicable resource insights in business process management.
The resource analytics tool has reached a stable and fully functional prototype stage. All core functionalities presented in this paper, including metric computation, interactive filtering, visualizations, and model overlays, are fully operational. The tool has been tested extensively using multiple event logs, including a procurement event log provided by Fluxicon1. The prototype supports interactive analysis across all four resource dimensions and maintains responsiveness even with mid-sized logs.
The tool follows a modular architecture and supports input in both XES and CSV formats, each requiring event data with the following elements: Case ID, Start Timestamp, Complete Timestamp, Activity, Resource, and Role. Ensuring the presence of these attributes is necessary for the consistent computation of all four resource analysis dimensions—allocation, performance, workload distribution, and capacity utilization. We acknowledge, however, that real-world event logs may not always conform to this schema. At present, the tool assumes complete input and does not yet support logs with missing columns. As such, preprocessing (e.g., using tools like pm4py) is required to ensure compatibility. Enhancing the tool’s flexibility to handle incomplete data – through schema detection, adaptive visualizations, and user guidance – is planned for future development to broaden applicability and improve robustness.
Currently, the tool is stable for browser-based use with logs up to approximately 10,000 events. For larger-scale logs, performance optimizations are planned as part of ongoing development.
The resource analytics tool is implemented as a web-based application, for which the source code, along with setup instructions and example event logs, can be accessed via Github.23 The application can be deployed locally using standard development stacks and does not require specialized infrastructure. Extensive ReadMe is provided within the GitHub repository.
To support accessibility and reproducibility, a screencast is provided that demonstrates the tool’s main functionalities, including how to upload logs, explore metrics across the four analysis areas, and interact with the resulting visualizations.4
The presented visual analytics tool enables richer and more intuitive analysis of resource behavior in business processes. By integrating multiple resource-related metrics into a single interactive environment, the tool facilitates the detection of bottlenecks, workload imbalances, and capacity issues. Through a visual-first design approach with interactive features, the tool supports both exploratory and targeted analysis. By making resource behavior more transparent, it adds tangible value to both BPM research and practical process analysis.
Beyond the current capabilities, future work will extend the tool’s scope from descriptive resource analysis toward predictive allocation support, incorporating behavioral dynamics such as learning curves, overload patterns, task switching behavior, and fatigue efects. This would enable the tool to not only visualize resource performance retrospectively, but also anticipate resource suitability for upcoming tasks, supporting more proactive and adaptive resource management decisions.