A Mamba-based approximate conformance checking method
Yilin Lyu · 2024
Process mining uses event flow information from log files to generate process models for better management and optimization of target processes. Approximate conformance checking quantifies the deviation of process models from process logs and is important for process compliance checking, model quality assessment and business process optimization. Existing approximate conformance checking methods are mainly classified into rule-based methods, token replay methods, and alignment-based methods. These methods usually rely on known process structures and traditional machine learning and mathematical statistics knowledge, and cannot adapt to the situation where large-scale logs and process structures are unknown. The correlation information between different traces is not effectively extracted. The effect of different length traces on coding sparsity is also not well addressed. To address the above problems, we adopt machine learning for approximate conformance checking and propose a Mamba-based method MACC. MACC efficiently improves the approximate conformance checking performance of machine learning based regressors on large-scale system logs under the scenario of unknown process structure. Specifically, we introduce the feature embedding of Mamba based classifier on the trace embedding before input into regressor. Mamba is used as the base feature extraction module of classifier, and TCN is introduced to enhance the fine-grained feature mining capability, which effectively extracts the correlation information among different traces while maintaining the model recognition rate. Finally, a split-bucket strategy based on normal distribution is designed to dynamically adjust the length threshold division to reduce the coding sparsity problem caused by longer traces in fixed-length coding. Comparative experiments are conducted on multiple machine learning methods and vanilla fitness-obtaining methods, and the proposed method achieves better results with all metrics on the BPIC2019. Also, multiple ablation experiments are conducted to discuss the effectiveness of the model structure. Comprehensive experiments demonstrated the effectiveness of the proposed method.