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Record W2768733040 · doi:10.3934/bdia.2017016

An ontological account of flow-control components in BPMN process models

2017· article· en· W2768733040 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBig Data and Information Analytics · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsTD Bank GroupYork University
Fundersnot available
KeywordsBusiness Process Model and NotationPetri netComputer scienceXPDLProgramming languageBusiness processProcess modelingBusiness process modelingSoftware engineeringTheoretical computer scienceWorkflowDatabaseWork in processWorkflow management system

Abstract

fetched live from OpenAlex

The Business Process Model and Notation (BPMN) has been widely adopted in the recent years as one of the standard languages for visual description of business processes. BPMN however does not include a formal semantics, which is required for formal verification and validation of behaviors of BPMN models.Towards bridging this gap using first-order logic, we in this paper present an ontological/formal account of flow-control components in BPMN, using Situation Calculus and Petri nets. More precisely, we use SCOPE (Situation Calculus Ontology of PEtri nets), developed from our previous work, to formally describe flow-control related basic components (i.e., events, tasks, and gateways) in BPMN as SCOPE-based procedures. These components are first mapped from BPMN onto Petri nets.Our approach differs from other major approaches for assigning semantics to BPMN (e.g., the ones applying communicating sequential processes, or abstract state machines) in the following aspects. Firstly, the approach supports direct application of automated theorem proving for checking theory consistency or verifying dynamical properties of systems. Secondly, it defines concepts through aggregation of more basic concepts in a hierarchical way thus the adoptability and extensibility of the models are presumably high. Thirdly, Petri-net-based implementation is completely encapsulated such that interfaces between the system and its users are defined completely within a BPMN context. Finally, the approach can easily further adopt the concept of time.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.015
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.141
GPT teacher head0.298
Teacher spread0.156 · 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