Summary of Snap: Semantic Stories For Next Activity Prediction, by Alon Oved et al.
SNAP: Semantic Stories for Next Activity Prediction
by Alon Oved, Segev Shlomov, Sergey Zeltyn, Nir Mashkif, Avi Yaeli
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel AI model called SNAP (Semantic Contextual Stories from Process Event Logs) to predict the next activity in an ongoing business process. The existing state-of-the-art AI models for business process prediction do not fully utilize available semantic information within process event logs, which is crucial for optimizing resource allocation and enhancing operational efficiency. To address this gap, the authors construct semantic contextual stories from historical event logs using language foundation models and leverage them for next activity prediction. Experimental results show that SNAP significantly outperforms nine state-of-the-art models on six benchmark datasets, especially those with high levels of semantic content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict what will happen next in a business process, like when a customer makes a payment or returns an item. This is important because it allows businesses to make better decisions and be more efficient. Right now, AI models can’t fully use the information they have about what’s happened so far in a process. To fix this, the authors created a new way to use language models to tell stories about what’s happened in a business process. Then, they used these stories to predict what will happen next. They tested their method on six different sets of data and it did much better than nine other AI methods. |
