基于去标记案例重组和细粒度图像立方体动作引擎的业务流程下一活动预测方法

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Ruoyuan Zhang, Xianwen Fang, Ke Lu, Xiwei Zhang
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引用次数: 0

摘要

预测性业务流程监控通过实时预测业务流程的下一个活动来发现业务执行中的异常情况,从而帮助企业及时调整和优化业务流程。现有的研究通常关注事件日志中单个轨迹的序列信息或流程模型的结构信息,而忽略了流程中的上下文关联信息以及现有和潜在的操作冲突对下一个活动预测准确性的影响。为了解决这些问题,我们提出了一种将跟踪案例重组和扩展与细粒度图像立方体约束动作引擎相结合的下一个活动预测方法。该方法解决了单行道上的病例数有限和编码图像中缺乏稀疏像素信息的问题。首先,删除用例的标签,并根据上下文依赖关系重新组织用例,扩展跟踪中的用例数量。然后,利用graian角场(GAF)进行细粒度图像编码,丰富编码后图像的内容。构造约束多维数据集约束动作引擎,使用联机分析处理(Online Analytical Processing, OLAP)操作约束流程方向、监控操作冲突并选择正确的流程方向。最后,在四个真实事件日志上的实验结果表明,该方法在预测下一个活动的准确性方面优于基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Business Process Next Activity Prediction Method Utilizing Remove Marked Case Reorganization and Fine-Grained Image Cube Action Engine

Predictive business process monitoring discovers anomalies in business execution by predicting the next activity of a business process in real time, thereby helping enterprises to adjust and optimize business processes in a timely manner. Existing research usually focuses on the sequence information of a single trace in event logs or the structural information of process models, while ignoring the contextual correlation information in the process and the impact of existing and potential operational conflicts on the accuracy of the next activity prediction. To address these issues, we propose a next activity prediction method that combines trace case reorganization and expansion with a fine-grained image cube constraint action engine. This method addresses the problem of limited case numbers in a single trace and the lack of sparse pixel information in the encoded image. First, the labels of cases are removed, and cases are reorganized based on context dependencies, expanding the number of cases in the trace. Then, Gramian Angular Field (GAF) is used for fine-grained image encoding to enrich the content of the encoded image. A constraint cube constraint action engine is constructed, and Online Analytical Processing (OLAP) operations are used to constrain the process direction, monitor operational conflicts, and select the correct process direction. Finally, experimental results on four real event logs show that the proposed method outperforms the baseline methods in terms of the accuracy of next activity prediction.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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