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Area of Science:

  • Business Process Management
  • Computer Vision
  • Data Mining

Background:

  • Traditional business process extraction relies on structured data (logs), limiting application to unstructured formats like images and videos.
  • Existing methods often yield process models lacking analytical consistency, leading to singular interpretations.
  • Video data offers a rich, albeit unstructured, source for capturing real-world business operations.

Purpose of the Study:

  • To propose a novel method for extracting business process models directly from video data.
  • To address the limitations of traditional models in handling unstructured data.
  • To enhance the analytical consistency and accuracy of extracted process models.

Main Methods:

  • Developed a method encompassing video data preprocessing, action localization and recognition, and conformance verification against predetermined models.
  • Employed graph edit distances and adjacency relationships (GED_NAR) to calculate similarity between extracted and predefined models.
  • Focused on extracting process models from video to ensure alignment with actual business operations.

Main Results:

  • The process model extracted from video data demonstrated superior alignment with actual business operations compared to models derived from noisy process logs.
  • The proposed method successfully extracts process models from unstructured video data, a significant advancement over log-based techniques.
  • Analysis confirmed improved consistency and accuracy in process models generated from video.

Conclusions:

  • Extracting business process models from video data is feasible and offers significant advantages over traditional log-based methods.
  • The proposed video-based approach enhances the accuracy and analytical consistency of process models.
  • This method opens new avenues for process mining in scenarios rich with visual data.