ODE: An Ontology for Numerical Design of Experiments

Blondet, Gaëtan; Duigou, Julien Le; Boudaoud, Nassim

doi:10.1016/j.procir.2016.04.199

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Furthermore, dedicated mark-up languages, e.g., SED-ML [6] based on XML, pure functional programming languages [17], and ontologies have proven to be competent in specifying and managing simulation experiments. For this study, it is of interest to achieve the replicability of the experiments developed in diverse DSLs, ontologies [18], or other formal standards and yet continue being compatible with the scientific community.…”

Section: Related Workmentioning

confidence: 99%

Hypothesis-Driven Simulation Experiments with an Extension to SED-ML

Cam¹,

Oğuztüzün²,

Yılmaz³

2022

Int. j. simul. model.

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Most of the frameworks or assistance systems for experiment specification do not provide a process explicitly based on formally specified hypotheses. This deficiency leads to inaccurate or insufficient record of an experiment, decreasing the trustworthiness and reproducibility of the experiment. Moreover, the wide variety of models, metamodels, tools, and data for experimentation requires Global Model Management (GMM) that is utilizing Model-Driven Engineering techniques, facilitates documentation, sharing, reusability, and replicability of simulation experiments. In this study, we strive to illustrate how to support simulation experimentation with hypotheses as a scientific workflow through GMM with an extension to the Simulation Experiment Description Mark-up Language (SED-ML). In particular, we present a megamodel built to serve as a repository to manage the artefacts employed in a simulation experiment. Based on the SED-ML, and enriched with hypothesis handling, our megamodel attempts to address all the phases of a simulation experiment, including specification, input data generation, execution, and output data analysis.

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Section: Related Workmentioning

confidence: 99%

Hypothesis-Driven Simulation Experiments with an Extension to SED-ML

Cam¹,

Oğuztüzün²,

Yılmaz³

2022

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…For the definition of a DoE three sampling methods, called 'types of DoE' are differentiated, namely marginal constraints like Latin Hypercube, factorial designs and low discrepancy or quasi random, e.g. Halton [2]. Subsequently further information requirements are added, including the number of experiments, an initial model, one or more factors, one or more outputs, a surrogate model and some analysis methods [3].…”

Section: Semantic Representations Of Doementioning

confidence: 99%

“…Subsequently further information requirements are added, including the number of experiments, an initial model, one or more factors, one or more outputs, a surrogate model and some analysis methods [3]. Also constraints like the limited amount of time or thresholds like accuracy of the predictions and surrogate model predictivity are supposed to be specified [2] and extended by a maximum number of experiments [3]. Nevertheless, the proposed rules and concept descriptions do not allow for the deduction of a subset of DoE types fulfilling the set of constraints.…”

Section: Semantic Representations Of Doementioning

confidence: 99%

Ontology-based Design of Experiments on Big Data Solutions

Zocholl,

Camossi,

Jousselme

et al. 2019

Preprint

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Big data solutions are designed to cope with data of huge Volume and wide Variety, that need to be ingested at high Velocity and have potential Veracity issues, challenging characteristics that are usually referred to as the "4Vs of Big Data". In order to evaluate possibly complex big data solutions, stress tests require to assess a large number of combinations of sub-components jointly with the possible big data variations. A formalization of the Design of Experiments (DoE) on big data solutions is aimed at ensuring the reproducibility of the experiments, facilitating their partitioning in sub-experiments and guaranteeing the consistency of their outcomes in a global assessment. In this paper, an ontology-based approach is proposed to support the evaluation of a big data system in two ways. Firstly, the approach formalizes a decomposition and recombination of the big data solution, allowing for the aggregation of component evaluation results at inter-component level. Secondly, existing work on DoE is translated into an ontology for supporting the selection of experiments. The proposed ontology-based approach offers the possibility to combine knowledge from the evaluation domain and the application domain. It exploits domain and inter-domain specific restrictions on the factor combinations in order to reduce the number of experiments. Contrary to existing approaches, the proposed use of ontologies is not limited to the assertional description and exploitation of past experiments but offers richer terminological descriptions for the development of a DoE from scratch. As an application example, a maritime big data solution to the problem of detecting and predicting vessel suspicious behaviour through mobility analysis is selected. The article is concluded with a sketch of future works.

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Ontological CFD-repository

Zenkin¹

2018

Proceedings of ISP RAS

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Based on the RDF-storage, a software tool was developed for creating a knowledge base containing information about the CFD-calculations performed. The software is a set of scripts written in bash and python, which are published under the GNU GPL3 license. The tool is designed to support the user when making research studies that do not have a strict, predefined design of experiment or problem solving algorithm. To formalize the description of the calculation stored in the knowledge base, an ontology is created that serves as the information model for the calculation. As an auxiliary mechanism for carrying out an automated comparison of calculations with each other (a mechanism of "comparators" and "features") was developed and also described in the article. In addition to the systematized data storage, the complex provides the possibility of their automated and semi-automated analysis, including the presentation of a set of calculations in the form of an undirected graph, the construction of flat and spatial dependencies, the search for similar calculations, etc. The article gives examples of data processing results for the project on design of channel in the cylinder head of a piston engine.

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ODE: An Ontology for Numerical Design of Experiments

Cited by 3 publications

References 15 publications

Hypothesis-Driven Simulation Experiments with an Extension to SED-ML

Hypothesis-Driven Simulation Experiments with an Extension to SED-ML

Ontology-based Design of Experiments on Big Data Solutions

Ontological CFD-repository

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