Advances in Process Optimization: A Comprehensive Survey of Process Mining, Predictive Process Monitoring, and Process-Aware Recommender Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866912240477667328 |
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| author | Khan, Asjad Ghose, Aditya Dam, Hoa Syed, Arsal |
| author_facet | Khan, Asjad Ghose, Aditya Dam, Hoa Syed, Arsal |
| contents | Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_10398 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Advances in Process Optimization: A Comprehensive Survey of Process Mining, Predictive Process Monitoring, and Process-Aware Recommender Systems Khan, Asjad Ghose, Aditya Dam, Hoa Syed, Arsal Other Computer Science Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization. |
| title | Advances in Process Optimization: A Comprehensive Survey of Process Mining, Predictive Process Monitoring, and Process-Aware Recommender Systems |
| topic | Other Computer Science |
| url | https://arxiv.org/abs/2301.10398 |