{"id":10401,"date":"2026-07-01T12:05:43","date_gmt":"2026-07-01T12:05:43","guid":{"rendered":"https:\/\/www.dab-europe.com\/?post_type=articles&p=10401"},"modified":"2026-07-06T13:11:58","modified_gmt":"2026-07-06T13:11:58","slug":"explainable-process-mining-kiga-wrap-up-from-research-to-grounded-ai-explanations","status":"publish","type":"articles","link":"https:\/\/www.dab-europe.com\/en\/articles\/explainable-process-mining-kiga-wrap-up-from-research-to-grounded-ai-explanations\/","title":{"rendered":"Explainable Process Mining: KIGA Wrap-Up from Research to Grounded AI Explanations"},"content":{"rendered":"\n
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Explainable Process Mining helps organizations make complex process analyses based on SAP event logs transparent, understandable, and actionable. This is exactly where the BayVFP research project KIGA<\/a> comes in: it demonstrates how Process Mining, explainable AI (XAI), and generative AI can be combined to better explain data-driven decisions and enable more informed analysis of business processes.

SAP processes generate a lot of valuable data. But turning that data into trusted process decisions is not always straightforward. Event logs can contain many variants, exceptions, loops, skipped steps, and deviations. Process Mining helps make these patterns visible. Yet when the result is a dense process model, the next challenge begins: how can business users, process owners, and analysts understand what they are seeing?

This question shaped the KIGA research project<\/strong> over the past several years. The project investigated how explainable Process Mining can enhance process analysis by making it more transparent, explainable, and easier to apply in practice. For dab, this topic directly connects to our work with enterprise data, SAP event logs<\/a>, analytics, auditability, and business process improvement. KIGA provided us with the opportunity to explore new methods at the intersection of Process Mining, explainable AI, semantic event data, and generative AI for Process Mining.<\/p>\n\n\n\n

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KIGA at a glance<\/h2>\n\n\n\n

KIGA focused on one central goal: making complex Process Mining results easier to understand and verify. The project explored how to enrich event logs with additional process knowledge, how to group similar process executions into meaningful clusters, and how to explain these clusters in a way that supports human analysis. The latest demonstrator brings these ideas together in an interactive dashboard. It combines Process Mining, explainable clustering, conformance checking, and an LLM-based chat interface that uses defined function calls instead of free-form guessing.<\/p>\n\n\n\n

\"Interactive\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button>
Figure 1: Interactive dashboard displaying the Directly Follows Graph visual, key metrics, conformance charts, and a natural language chat interface.<\/strong><\/figcaption><\/figure>\n\n\n\n
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From complex event logs to understandable process insights<\/h2>\n\n\n\n

At the beginning of KIGA, the main problem was already clear: Process Mining often produces models that are technically correct but difficult to interpret.<\/p>\n\n\n\n

\"Spaghetti\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button>
Figure 2: Spaghetti graph from Process Mining on the publicly available Road Traffic Fine Management Process dataset<\/strong><\/figcaption><\/figure>\n\n\n\n

A process model may show hundreds of variants. It may reveal rework, missing activities, unusual paths, and deviations from an expected process flow. For a Process Mining expert, this can already be a significant challenge. For business users, auditors, or process owners, it can be even more challenging to determine what matters. <\/p>\n\n\n\n

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The project, therefore, focused on three connected ideas:<\/p>\n\n\n\n