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Record W6969141368 · doi:10.5281/zenodo.8303080

Benchmark Lab for Hypercompliance Properties on Event Logs

2023· other· en· W6969141368 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeother
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsCorrectnessEvent (particle physics)Benchmark (surveying)TRACE (psycholinguistics)Set (abstract data type)Process (computing)Scripting languageDebugging

Abstract

fetched live from OpenAlex

This LabPal experimental environment contains the benchmark for hypercompliance properties on event logs, as described in the paper <em>Hypercompliance: Business Process Compliance Across Multiple Executions</em>, published at the EDOC 2023 conference. <strong>Context</strong> 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. <strong>Description</strong> 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] <strong>Dataset contents</strong> 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 <strong>Running LabPal</strong> To start the lab, open a terminal window and type at the command line: <pre><code>java -jar hypercompliance-lab-1.0.jar --autostart</code></pre> You should see something like this: <pre><code>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</code></pre> 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. <strong>Using the web interface</strong> 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<br> contains. You can follow the progress of these experiments by going to the<br> Status page and refreshing it periodically. At any point, you can look at<br> 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0040.000
Open science0.0060.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.044

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.

Opus teacher head0.176
GPT teacher head0.362
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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