Employing Data and Process Mining Techniques for Redundancy Detection and Analystics in Business Processes
Bibliographic record
Abstract
The detection, quantification, and scrutiny of redundancies within business processes is pivotal in achieving cost reduction, enhancing efficiency, and ensuring compliance.Redundancies, often leading to inefficiencies, result in escalated costs and errors, thereby detrimentally influencing an organization's overall performance.To counter these issues, data mining and process mining techniques offer promising solutions by identifying and analyzing process redundancies.Data mining, an approach devoted to the analysis of large datasets in order to discern patterns, relationships, and anomalies, has been applied to business processes.It provides insights into redundancies by scrutinizing process-related data, such as event logs, thereby revealing patterns in task executions that may indicate redundancies.In contrast, process mining employs event logs to generate a process model mirroring the actual execution of a process.This actual process model is subsequently contrasted against an expected process model, facilitating the identification of redundancies such as unnecessary activities or loops.Cluster analysis, a technique employed in both data mining and process mining, is exemplified for its capacity to group similar process instances or models based on specified attributes or characteristics.The application of cluster analysis aids in the identification of redundant process models or similar process patterns, thereby enabling further comparison and optimization.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".