Organizations increasingly rely on Process Performance Indicators (PPIs) to evaluate how effectively their business processes achieve strategic and operational goals. Recent approaches compute PPIs directly from event logs leveraging the increasing availability of event data in organizations, but they assume that PPIs have been previously defined using well-established reference models and that their linkage to the event log has been explicitly specified. However, this definition of appropriate PPIs remains challenging, as managers often lack detailed insights into real process executions and must adapt generic frameworks to organizational contexts. To address this gap, this paper introduces PPIPilot, an approach and tool that supports managers in defining measurable PPIs by suggesting a set of relevant PPIs and their computations directly from a business process description, an associated event log, and a specified organizational goal. PPIPilot leverages large language models for contextual PPI suggestion and structured translation, operating through a three-component pipeline of suggestion, translation, and computation, with a fallback mechanism to address computation errors. The quantitative evaluation shows that PPIPilot has a strong ability to suggest and compute PPIs, and that the fallback mechanism reduces failures, increasing the share of PPIs that can be computed without errors. Complementing these findings, an in-depth qualitative study indicates that users find the tool intuitive, transparent and helpful for easing both the conceptual and technical challenges of defining PPIs.

Abstract Knowledge-intensive processes represent a particularly challenging scenario for process mining. The flexibility that such processes allow constitutes a hurdle as they are hard to capture in a single model. To tackle this problem, multiple visual representations of the same processes could be beneficial, each addressing different information dimensions according to the specific needs and background knowledge of the concrete process workers and stakeholders. In this paper, we propose, describe, and evaluate a framework, named , that leverages visual analytics for the interactive visualization of multi-faceted process information, aimed at supporting the investigation and insight generation of users in their process analysis tasks. is based on a multi-layer visualization methodology that includes a visual backdrop that provides context and an arbitrary number of superimposed and on-demand dimension layers. This arrangement allows our framework to display process information from different perspectives and to project this information onto a domain-friendly representation of the context in which the process unfolds. We provide an in-depth description of the approach’s founding principles, deeply rooted in visualization research, that justify our design choices for the whole framework. We demonstrate the feasibility of the framework through its application in two use-case scenarios in the context of healthcare and personal information management. Plus, we conducted qualitative evaluations with potential end users of both scenarios, gathering precious insights about the efficacy and applicability of our framework to various application domains.