Summary of Anomaly Correction Of Business Processes Using Transformer Autoencoder, by Ziyou Gong et al.
Anomaly Correction of Business Processes Using Transformer Autoencoder
by Ziyou Gong, Xianwen Fang, Ping Wu
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel business process anomaly correction method based on Transformer autoencoders, which can efficiently detect and correct anomalies in event log records. The approach uses self-attention mechanisms and an autoencoder structure to process event sequences of arbitrary length, producing corrected business process instances. Anomaly detection is reformed as a classification problem through self-supervised learning, eliminating the need for threshold setting. Experimental results on real-life event logs demonstrate superior performance compared to previous methods in terms of anomaly detection accuracy, correction results, and running efficiency. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make sure business processes run smoothly by finding and fixing mistakes in what happens during those processes. Right now, we have two main ways to do this: predict what will happen next or use special math to find patterns. But these methods aren’t perfect – they can miss problems or need someone to decide when something is wrong. The new method uses a special kind of computer model that looks at the order of things happening and makes corrections on its own. It’s like having a super-smart assistant who can fix mistakes without needing rules or guidelines. Tests with real-life data show this new way works better than others, making it a useful tool for businesses. |
Keywords
» Artificial intelligence » Anomaly detection » Autoencoder » Classification » Self attention » Self supervised » Transformer