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REACH: Researching Efficient Alignment-based Conformance Checking
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AI
DOI：10.1016/j.eswa.2023.122467.png)
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摘要
En 中文
Conformance checking techniques compare how a process is supposed to be executed according to a model with how it is executed in reality according to an event log. Alignment-based approaches are the most successful solutions for conformance checking. Optimal alignments are a way of finding the best match between the real and the modeled behavior and identifying the differences. However, finding these optimal alignments is a challenging task, especially for complex cases where the log and the model have many events and paths. The difficulty lies in the computational complexity required to find these alignments. To address this problem, we propose an efficient algorithm named REACH based on the A* search algorithm. The core components of the proposal are the use of a partial reachability graph for faster execution of process models for alignment computation and a set of optimization techniques for reducing the number of states explored by the A* algorithm. These improve performance by both reducing the required computation time per state and the number of states to process respectively. To evaluate the performance and scalability, we conducted tests using 227 pairs of logs and models, comparing the results obtained with those from 10 state-of-the-art approaches. Results show that REACH outperforms the other proposals in runtimes, and even aligns logs and models that no other algorithm is able to align.
Keyword:
Process mining
Conformance checking
Alignments
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IF:
7.5
论文数:
3.0W
被引数:
10.2W
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