Benchmark Lab for Hypercompliance Properties on Event Logs
Bibliographic record
Abstract
This LabPal experimental environment contains the benchmark for hypercompliance properties on event logs, as described in the paper Hypercompliance: Business Process Compliance Across Multiple Executions, published at the EDOC 2023 conference. Context Compliance checking is an operation that assesses whether every execution trace of a business process satisfies a given correctness condition. Our work introduces the notion of a hyperquery, which involves multiple traces from a log at the same time. A specific instance of a hyperquery is a hypercompliance condition, which is a correctness requirement that involves the entire log instead of individual process instances. This lab proposes a benchmark for an extension of the BeepBeep 3 event stream engine designed to evaluate hyperqueries on event logs. It evaluates various hyperqueries on event logs that are either synthetic or sourced from real-world online log repositories. Among the elements evaluated are the total and progressive running time needed to evaluate a query, as well as the amount of memory consumed. Description This archive contains an instance of LabPal, an environment for running experiments on a computer and collecting their results in a user-friendly way. The author of this archive has set up a set of experiments, which typically involve running scripts on input data, processing their results and displaying them in tables and plots. LabPal is a library that wraps around these experiments and displays them in an easy-to-use web interface. The principle behind LabPal is that all the necessary code, libraries and input data should be bundled within a single self-contained JAR file, such that anyone can download and easily reproduce someone else's experiments. All the plots and other data values mentioned in the paper are automatically generated by the execution of this lab. The lab also provides additional tables and plots that could not fit into the manuscript. Detailed instructions can be found on the LabPal website, [https://liflab.github.io/labpal] Dataset contents In addition to this Readme, the dataset is made of three files: hypercompliance-lab-1.0.jar is the runnable instance of the lab hypercompliance-lab-1.0-sources.jar contains the source code of the BeepBeep library and the benchmark hypercompliance-lab-1.0-javadoc.jar contains the documentation of the BeepBeep library and the benchmark Running LabPal To start the lab, open a terminal window and type at the command line: java -jar hypercompliance-lab-1.0.jar --autostart You should see something like this: LabPal 2.99 - A versatile environment for running experiments (C) 2014-2022 Laboratoire d'informatique formelle Université du Québec à Chicoutimi, Canada Please visit http://localhost:21212/index to run this lab Hit Ctrl+C in this window to stop Open a web browser and type `http://localhost:21212/index` in the address bar. This should lead you to the main page of LabPal's web control panel. Using the web interface A detailed explanation on the use of the LabPal web interface can be found in this YouTube video. A lab is made of a set of *experiments*, each corresponding to a specific set of instructions that runs and generates a subset of all the benchmark's results. Results from experiments are collected and processed into various auto-generated tables and plots. The lab is instructed to immediately start running all the expermients it contains. You can follow the progress of these experiments by going to the Status page and refreshing it periodically. At any point, you can look at the results of the experiments that have run so far. You can do so by: Going to the Plots (5th button in the top menu) or the Tables (6th button) page and see the plots and tables created for this lab being updated in real time Going back to the list of experiments, clicking on one of them and getting the detailed description and data points that this experiment has generated Once the assistant is done, you can export any of the plots and tables to a file, or the raw data points by using the Export button in the Status page.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".