Deriving collaborative models from business process models

Giraldo, Fáber D.; Alzate, Alexander; Duarte, Laura; Tobón, Mónica Lorena; Hoyos, Briyan

doi:10.1109/colomcc.2011.5936278

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Giraldo et al [10] propose an approach to transform BPMN models to a model with more precision called CIAM. They emphasize in the importance of capturing the knowledge as early as possible, for example in early meeting as we propose.…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Approach to specify Kernel Sentences using Natural Language

Antonelli,

Fernandez,

Ruffolo

et al. 2022

Anais Do Workshop Em Engenharia De Requisitos

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Requirements engineering is a critical part of software development. Errors in the requirements, if not found and corrected early in the engineering process, become costly problems later on. Analysts commonly rely on Use Cases or Users Stories to capture requirements. However, there is domain knowledge that these artifacts donâ€™t capture well (for example, business rules and given-then-when scenarios). Such domain knowledge is generally distributed among multiple stakeholders and domain experts with complementing perspectives. Therefore, it is important to use a collaborative technique with a simple artifact to acquire and validate their knowledge. Kernel sentences is a linguistic definition about small sentences (with only one verb) written in active voice. Some authors relate kernel sentences to business rules. We argue that kernel sentences are adequate to use in the collaborative acquisition and they can be used as the input to produce more complex artifacts. This paper proposes a collaborative approach to acquire and validate kernel sentences. The process has three main activities: acquisition of the kernel sentences, validation of them, and assessment of the activity of the experts who participate in the activity. This paper also describes a prototype to support the process. Finally, the paper shows the result of a preliminary evaluation with promising results about the applicability of the process.

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Section: Related Workmentioning

confidence: 99%

A Collaborative Approach to specify Kernel Sentences using Natural Language

Antonelli,

Fernandez,

Ruffolo

et al. 2022

Anais Do Workshop Em Engenharia De Requisitos

View full text Add to dashboard Cite

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“…A detailed description of the synthesis of the mesoporous N-doped carbon D-96-7 has been published elsewhere [33]. Briefly, the material was synthesized via direct route assembly using pluronic F-127 as surfactant and aniline as carbon source.…”

Section: Sample Preparation and Characterizationmentioning

confidence: 99%

“…Here, we present a combined neutron spectroscopy and diffraction study of the adsorption and diffusional dynamics of molecular hydrogen in a novel N-doped highly porous carbon that one of the authors has recently produced (from now on referred to as D-96-7) [32,33]. This material was synthesized by direct route, omitting the need for inorganic templates.…”

Section: Introductionmentioning

confidence: 99%

Ultra-fast diffusion of hydrogen in a novel mesoporous N-doped carbon

Bahn

Giraldo

Kuznetsov

et al. 2020

Carbon

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The diffusional behaviour of H 2 adsorbed in a novel mesoporous N-doped carbon material and on exfoliated graphite has been studied with quasi-elastic neutron scattering. While the activation energies for diffusion obtained for exfoliated graphite was in line with prior results, H 2 in the mesoporous carbon showed an exceptionally low diffusion barrier and behaved qualitatively like liquid hydrogen. Complementary results from neutron and X-ray diffraction as well as from isothermal nitrogen sorption studies provide a full characterisation of this novel mesoporous carbon.

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Deriving collaborative models from business process models

Cited by 2 publications

References 4 publications

A Collaborative Approach to specify Kernel Sentences using Natural Language

A Collaborative Approach to specify Kernel Sentences using Natural Language

Ultra-fast diffusion of hydrogen in a novel mesoporous N-doped carbon

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