mlsanityData to 7 Oct 2026

Process Mining for Unstructured Data: Challenges and Research Directions

arXiv 2401.13677 ↗First posted 30 Nov 2023cs.DB cs.AI cs.LGcited by 4

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Abstract (arXiv)

The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 45 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

10 of 45 references.

References · 10

  1. 2019Image and Video Compression with Neural Networks: A Reviewcited by 82
  2. 2020Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoringcited by 20
  3. 2023ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Trainingcited by 13
  4. 2016Event Abstraction for Process Mining using Supervised Learning Techniquescited by 9
  5. 2021Privacy and Confidentiality in Process Mining -- Threats and Research Challengescited by 6
  6. 2020Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Miningcited by 5
  7. 2021PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Datacited by 5
  8. 2022Event-Case Correlation for Process Mining using Probabilistic Optimizationcited by 4
  9. 2022SKTR: Trace Recovery from Stochastically Known Logscited by 4
  10. 2022Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniquescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Recent Advances in Data-Driven Business Process Managementcited by 2
  2. 2024Ranking the Top-K Realizations of Stochastically Known Event Logscited by 0
  3. 2026All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streamscited by 0
  4. 2026Interpretable experiential learning based on state history and global feedbackcited by 0