Bibliographic record
Abstract
The authors propose a co-clustering approach to extract workflow models by analyzing event logs. The authors consider two major issues that are overlooked by most of the existing process mining approaches. First, a complex system typically runs multiple workflow models, all of which share the same log system. However, current approaches mainly focus on learning a single workflow model from event logs. Second, most systems support multi-users and each user is typically associated with (or use) certain number of operation sequences, which may follow one or more than one workflow models. Users can thus be leveraged as an important context when learning workflow models. However, this is not considered by current approaches. Therefore, the authors propose to learn User Behavior Pattern (UBP) that reflects the usage pattern of a user when accessing a business process system and exploit it to discover multiple workflow models from the event log of a complex system. The authors model a UBP as a probabilistic distribution on sequences, which allows computing the similarity between UBPs and sequences. The authors then co-cluster users and sequences to generate two types of clusters: user clusters that group users sharing similar UBP, and sequence clusters that group sequences that are the instances of the same workflow models. The workflow model can then be learned by analyzing its instances. The authors conducted a comprehensive experimental study to evaluate the effectiveness and efficiency of the proposed approach.
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.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".