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.


The RLR framework for change management and conflict resolution.
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fig11: The RLR framework for change management and conflict resolution.

Mentions: The RLR multiagent framework [14] (RLRstands for: representation, legitimation, reproduction) (Figure 11) aimed atcapturing, tracking, representing, and managing the changes in a formal andconsistent way, enabling the system to generate reproducible results usingchange capture agents, reasoning agents, learning agents, and negation agents. Change capture agents are responsible for discovering, capturing, and trackingchanges in ontology, by processing the change logs. The change logs accumulate importantdata about various types of changes. In RLR, a learner agent uses thesehistorical records of changes that occur over and over in a change process toderive a pattern to estimate the rate and direction of future changes for asystem by generating rules or models. The reasoner (which verifies the resultsof a change) and negotiation agents can change the rules generated and sendmodifications to the learning agent. Negotiation takes place when agents withconflicting interests want to cooperate. In RLR, the negotiation agent acts asa mediator allowing the ontology engineer and other autonomous agents tonegotiate the best possible realization of a specific change, while maximizingthe benefits and minimizing the loss caused by such a change. A human expert maythen browse the results, propose actions, and decide whether to confirm,delete, or modify the proposals, in accordance with the intention of theapplication. In RLR, negotiation is defined based on the conceptual model ofargumentation [28], where an argumentis described as a piece of information allowing an agent to support and justify its negotiation stance or affect another agent's position through acommunication language and a formal protocol [28]. The negotiation protocol canformally provide the necessary rules [29] (i.e., rules for admissions,withdrawals, terminations) for negotiation dialog among participants. In our approach, we have partiallyadapted the architecture of the argumentativenegotiating agent described at [30].


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)

The RLR framework for change management and conflict resolution.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig11: The RLR framework for change management and conflict resolution.
Mentions: The RLR multiagent framework [14] (RLRstands for: representation, legitimation, reproduction) (Figure 11) aimed atcapturing, tracking, representing, and managing the changes in a formal andconsistent way, enabling the system to generate reproducible results usingchange capture agents, reasoning agents, learning agents, and negation agents. Change capture agents are responsible for discovering, capturing, and trackingchanges in ontology, by processing the change logs. The change logs accumulate importantdata about various types of changes. In RLR, a learner agent uses thesehistorical records of changes that occur over and over in a change process toderive a pattern to estimate the rate and direction of future changes for asystem by generating rules or models. The reasoner (which verifies the resultsof a change) and negotiation agents can change the rules generated and sendmodifications to the learning agent. Negotiation takes place when agents withconflicting interests want to cooperate. In RLR, the negotiation agent acts asa mediator allowing the ontology engineer and other autonomous agents tonegotiate the best possible realization of a specific change, while maximizingthe benefits and minimizing the loss caused by such a change. A human expert maythen browse the results, propose actions, and decide whether to confirm,delete, or modify the proposals, in accordance with the intention of theapplication. In RLR, negotiation is defined based on the conceptual model ofargumentation [28], where an argumentis described as a piece of information allowing an agent to support and justify its negotiation stance or affect another agent's position through acommunication language and a formal protocol [28]. The negotiation protocol canformally provide the necessary rules [29] (i.e., rules for admissions,withdrawals, terminations) for negotiation dialog among participants. In our approach, we have partiallyadapted the architecture of the argumentativenegotiating agent described at [30].

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.