From ODP viewpoint consistency to Integrated Formal Methods

Boiten, Eerke Albert; Derrick, John

doi:10.1016/j.csi.2011.10.015

Cited by 7 publications

(6 citation statements)

References 44 publications

(53 reference statements)

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“…They specifically obey a rich collection of mathematical laws, which make it possible to determine the behavior of the system [25,26]. This aspect of Z leverage, towards data-rich formalism of knowledge contents and the resulting artifacts, can be easily mapped to standard viewpoints of RM-ODP [32] (e.g., the information viewpoint). H. Bowman et al used Z notation for consistency checking of the two views in the information viewpoint [33].…”

Section: Motivation Of Formal Methodsmentioning

confidence: 99%

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Hussain¹,

Afzal²,

Taghizadehghalehjoughi³

et al. 2019

Preprint

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Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8\%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

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Section: Motivation Of Formal Methodsmentioning

confidence: 99%

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Hussain¹,

Afzal²,

Taghizadehghalehjoughi³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the authors point out that "a general framework must also take into account the work flow process, to allow consistency to be evaluated and reestablished periodically but not enforced all the time", and, though they consider that their approach is flexible enough to permit the development of such a framework, they do not undertake it. By contrast, work on RM-ODP, such as [4], takes the views as the primary artefacts. They serve as requirements on an all-encompassing model, which will only exist if the views are all consistent.…”

Section: Multi-view Modellingmentioning

confidence: 99%

Maintaining consistency in networks of models: bidirectional transformations in the large

Stevens

2019

Softw Syst Model

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The model-driven development of systems involves multiple models, metamodels and transformations, and relationships between them. A bidirectional transformation (bx) is usually defined as a means of maintaining consistency between "two (or more)" models. This includes cases where one model may be generated from one or more others, as well as more complex ("symmetric") cases where models record partially overlapping information. In recent years, binary bx, those relating two models, have been extensively studied. Multiary bx, those relating more than two models, have received less attention. In this paper, we consider how a multiary consistency relation may be defined in terms of binary consistency relations and how consistency restoration may be carried out on a network of models and relationships between them. In particular, we consider the circumstances under which we can prove non-interference between several bidirectional transformations that impact on the same model and how the use of a more refined notion of consistency can help in cases where this is not possible. In the process, we develop an abstract theory of parts of a model that are read or modified by a bidirectional transformation. We relate the work to megamodelling and discuss further research that is needed.

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“…They specifically obey a rich collection of mathematical laws, which make it poss ible to determine the behavior of the system [25,26]. This aspect of Z leverage, toward s data-rich formalism of knowledge contents and the resulting artifacts, can be easily mapped to standard viewpoints of RM-ODP [32] (e.g., the information viewpoint). H.…”

Section: Preliminariesmentioning

confidence: 99%

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Hussain¹,

Afzal²,

Malik³

et al. 2019

Preprint

View full text Add to dashboard Cite

Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

show abstract

From ODP viewpoint consistency to Integrated Formal Methods

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 7 publications

References 44 publications

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Maintaining consistency in networks of models: bidirectional transformations in the large

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

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