Limits...
Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory.

Shaban-Nejad A, Ormandjieva O, Kassab M, Haarslev V - Int J Telemed Appl (2009)

Bottom Line: With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain.At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility.The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Boulevard West, Montreal, QC, Canada H3G 1M8.

ABSTRACT
Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.

No MeSH data available.


Tracing the changes to the state spaces, classes, and methods.
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fig10: Tracing the changes to the state spaces, classes, and methods.

Mentions: The solution space category containsstate space SS (all potential statesincluding initial states), state transition ST (next state function), class C categoricalobjects, and methods arrows. The traceimplementation morphism tracesthe effect of the changes to artifact objects on the solution space objects. InFigure 10, for instance, we illustrate the refinement of an event from the artifactcategory to a state transition object ST. Moreover, each state transition ST isdefined on the state space SS (arrow ST_SS) linked by a function ST_C: ST → C to a class C. The state transitions are implemented by methods captured withthe function ST_M: ST → AP_M, and belonging to a class C (see function M_C). Theabove functions support the tracing mechanism and are captured formally in Figure10. The changes are then represented formallyin terms ofthe composition operator ∘; for instance, E_ST ∘ ST_SS ∘ ST_C will trace a change in dom E_ST (which is A_Event) to the codomain of ST_C (which is class C).


Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory.

Shaban-Nejad A, Ormandjieva O, Kassab M, Haarslev V - Int J Telemed Appl (2009)

Tracing the changes to the state spaces, classes, and methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2662492&req=5

fig10: Tracing the changes to the state spaces, classes, and methods.
Mentions: The solution space category containsstate space SS (all potential statesincluding initial states), state transition ST (next state function), class C categoricalobjects, and methods arrows. The traceimplementation morphism tracesthe effect of the changes to artifact objects on the solution space objects. InFigure 10, for instance, we illustrate the refinement of an event from the artifactcategory to a state transition object ST. Moreover, each state transition ST isdefined on the state space SS (arrow ST_SS) linked by a function ST_C: ST → C to a class C. The state transitions are implemented by methods captured withthe function ST_M: ST → AP_M, and belonging to a class C (see function M_C). Theabove functions support the tracing mechanism and are captured formally in Figure10. The changes are then represented formallyin terms ofthe composition operator ∘; for instance, E_ST ∘ ST_SS ∘ ST_C will trace a change in dom E_ST (which is A_Event) to the codomain of ST_C (which is class C).

Bottom Line: With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain.At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility.The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Boulevard West, Montreal, QC, Canada H3G 1M8.

ABSTRACT
Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.

No MeSH data available.