Predictive Process Monitoring Methods: Which One Suits Me Best?
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2018
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| _version_ | 1866911067188232192 |
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| author | Di Francescomarino, Chiara Ghidini, Chiara Maggi, Fabrizio Maria Milani, Fredrik |
| author_facet | Di Francescomarino, Chiara Ghidini, Chiara Maggi, Fabrizio Maria Milani, Fredrik |
| contents | Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1804_02422 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Predictive Process Monitoring Methods: Which One Suits Me Best? Di Francescomarino, Chiara Ghidini, Chiara Maggi, Fabrizio Maria Milani, Fredrik Artificial Intelligence Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques. |
| title | Predictive Process Monitoring Methods: Which One Suits Me Best? |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/1804.02422 |