An Efficient Computation of\nConvex Closure on Abstract Events
Bibliographic record
Abstract
The behaviour of distributed applications can be modeled as the occurrence of events and how these events relate to each other. Event data collected according to this event model can be visualized using process-time diagrams that are constructed from a collection of traces and events. One of the main characteristics of a distributed system is the large number of events that are involved, especially in practical situations. This large number of events, and hence large process-time diagrams, make distributed-system observation difficult for the user. However, event-predicate detection, a search mechanism able to detect and locate arbitrary predicates within a process-time diagram or event collection, can help the user to make sense of this large amount of data. Ping Xie used the convex-abstract event concept, developed by Thomas Kunz, to search for hierarchical event predicates. However, his algorithm for computing convex closure to construct compound events, and especially hierarchical compound events (i. e. , compound events that contain other compound events), is inefficient. In one case it took, on average, close to four hours to search the collection of event data for a specific hierarchical event predicate. In another case, it took nearly one hour. This dissertation discusses an efficient algorithm, an extension of Ping Xie?s algorithm, that employs a caching scheme to build compound and hierarchical compound events based on matched sub-patterns. In both cases cited above, the new execution times were reduced by over 94%. They now take, on average, less than four minutes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".