Enhancing Robotic Process Automation Task Selection: An Integrated Approach Leveraging Process Mining and Feature Extraction
Bibliographic record
Abstract
Robotic Process Automation (RPA), an emergent technology, is increasingly being utilized for the automation of straightforward and structured tasks, due to its time efficiency and cost effectiveness. As organizations strive to automate processes, it becomes imperative to discern the most suitable technology for each task to optimize investments in automation. The surge in RPA usage illuminates the challenge of task selection for automation. In response to this challenge, our study presents an integrated approach of process mining and feature extraction to enhance RPA task selection. Organizations provide feature weights, based on which corresponding tasks are extracted. Each task is subsequently ranked, and an overall task rank is computed by summing the products of feature weights and individual feature ranks. This procedure is iteratively performed for all tasks, culminating in a feature matrix, which constitutes the output of this framework. By leveraging historical process data, this combined approach allows for the identification of tasks that exhibit characteristics amenable to automation, such as high frequency, low variability, and distinct decision points. Furthermore, the extraction of task features enables the prioritization of tasks based on their potential for automation, complexity, and anticipated benefits. Through the analysis of process mining data, this study offers an empirical snapshot of organizational activities and suggests tasks that are amenable to RPA. This prioritization of suitable tasks for automation potentially enhances the success of RPA implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".