School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266000, China.
Sensors (Basel). 2023 Apr 7;23(8):3812. doi: 10.3390/s23083812.
Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs.
传统的业务流程提取模型主要依赖于日志等结构化数据，难以应用于图像和视频等非结构化数据，因此无法在许多数据场景中进行流程提取。此外，生成的流程模型缺乏流程模型的分析一致性，导致对流程模型的理解单一。为了解决这两个问题，提出了一种从视频中提取流程模型并分析流程模型一致性的方法。视频数据被广泛用于捕获业务运营的实际绩效，是业务数据的关键来源。从视频中提取流程模型并分析流程模型与预定义模型之间的一致性的方法包括视频数据预处理、动作放置和识别、预定模型和一致性验证。最后，使用图编辑距离和邻接关系（）计算相似度。实验结果表明，从视频中挖掘出的流程模型比从嘈杂的流程日志中推导出来的流程模型更符合业务实际执行情况。
