The methodology roles in the realization of a model development environment

Nance, Richard E.; Arthur, James D.

doi:10.1145/318123.318192

Cited by 16 publications

(5 citation statements)

References 21 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Balci [11] describes a model life cycle compatible with the conical methodology; we will use similar terminology to describe our modeling process. The primary objectives of the conical methodology are correctness, testability, adaptability, reusability, and maintainability [14]. This is a good match with our primary and secondary requirements.…”

Section: Applying a Methodologymentioning

confidence: 83%

“…Computational technology is also expected to change significantly during the lifetime of a model; models and the modeling process need to be insensitive to the runtime host and adaptable to high-performance computing techniques of the next 10 years. Using the terminology of Nance and Arthur [14], our modeling process must support correctness and testability; support adaptability, maintainability, and portability; and test throughout the model life cycle.…”

Section: Applying a Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Improving the Development Process for Eukaryotic Cell Cycle Models with a Modeling Support Environment

et al. 2003

View full text Add to dashboard Cite

Theoretical molecular biologists attempt to describe cellular processes and regulatory networks with continuous and discrete mathematical models. Previous practice has been to develop models largely by hand and then to validate them primarily by comparing time-series plots versus the observed experimental results. The authors report their experiences in designing and building a modeling support environment for cell cycle models. They describe improvements to the development process for molecular network models by (a) identifying the key elements of the existing modeling process, (b) incorporating simulation methodology into a revised modeling process, and (c) building and testing software that supports the revised modeling process.

show abstract

Section: Applying a Methodologymentioning

confidence: 83%

Section: Applying a Methodologymentioning

confidence: 99%

Improving the Development Process for Eukaryotic Cell Cycle Models with a Modeling Support Environment

et al. 2003

View full text Add to dashboard Cite

show abstract

“…As a result, Orca now hires people, pays taxes and contributes to the economical development of Southwest Virginia. The VSE technology transfer improves the national economical competitiveness in discrete-event simulation by way of the following business goals of Orca: (Balci 1986a(Balci , b, 1987(Balci , 1989(Balci , 1990(Balci , 1994Balci et al 1990;Balci and Nance 1985, b, 1989Nance 1983Nance ,1984Nance ,1987Nance ,1994Nance and Arthur 1988;Balci 1984, 1987) Lynne F. Barger Ph.D. 1992 (Box 1984) (Derrick 1988(Derrick , 1992Derrick and Balci 1994, 1997Derrick et al 1989) Chuck 1996 1987 (Frankel 1987;Frankel and Balci 1989) 1984 (Hansen 1984) Rajendra Harrichunder 1 M.S. 1 1994 1 (Harrichunder 1994) I…”

Section: Discussionmentioning

confidence: 99%

The Visual Simulation Environment technology transfer

Balcı

Bertelrud

Esterbrook

et al. 1997

Proceedings of the 29th Conference on Winter Simulation - WSC '97

Self Cite

View full text Add to dashboard Cite

show abstract

“…Portability, extensibility, and reusability are recognized model development objectives [Nance and Arthur 1988]. In some studies, efficient model execution is a primary development goal.…”

Section: Sources Of Representational Redundancymentioning

confidence: 99%

Redundancy in model specifications for discrete event simulation

Nance

Overstreet

Page

1999

ACM Trans. Model. Comput. Simul.

Self Cite

View full text Add to dashboard Cite

Although redundancy in model specification generally has negative connotations, we offer arguments for revising those convictions. Defining "representational redundancy" as the inclusion of any symbols not required to fulfill the study objectives, we cite several sources of redundancy, classified as accidental or intentional, that contribute positively to the model development tasks. Comparative benefits and detriments are discussed briefly. Focusing on the most interesting source of redundancy-that which is intentionally induced by a modeling methodology-we demonstrate that automated elimination of redundancy can actually improve model execution time. Using four models drawn from the literature that are easily understood, but which represent some differences in size and complexity, the direct execution of the graphical representations shows improvements over a base case ranging from 27.3 percent to 68.1 percent in execution time. Further, increasing improvement is realized with increasing model size and complexity. These results are encouraging because they suggest that modeling methodologies with automated model diagnosis can significantly reduce both execution and development time and cost.

show abstract

The methodology roles in the realization of a model development environment

Cited by 16 publications

References 21 publications

Improving the Development Process for Eukaryotic Cell Cycle Models with a Modeling Support Environment

Improving the Development Process for Eukaryotic Cell Cycle Models with a Modeling Support Environment

The Visual Simulation Environment technology transfer

Redundancy in model specifications for discrete event simulation

Contact Info

Product

Resources

About