Enhancing Robotic Process Automation Task Selection: An Integrated Approach Leveraging Process Mining and Feature Extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it