A framework for mapping UML class into XML data based on technical Specifications

Gherabi, Noreddine; Addakiri, Khaoula; Bahaj, Mohamed

doi:10.1109/icmcs.2012.6320174

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…support for IoT/CPS capabilities composition/decomposition through their enabled properties. The motivations include: a) the ability to address the composition of functional, business, human, trustworthiness, timing, data, boundaries, composition, and lifecycle concerns of an IoT or a CPS, and b) taking into consideration the complexity, discoverability, adaptability, and constructivity of the composite capabilities [38][39] [40]; c) enabling computation distribution of computation-intensive services running in cloud nodes to low computation nodes at the Fog/Edge level [252]; d) addressing composite services functional and qualitative properties during runtime to enable flexible and adaptable compositions [42] and e) incorporating composition-friendly ontologies and composition mechanisms in distributed environments through hierarchical structures such as classes and subclasses [35][36] [220]; f) providing automatic composition mechanisms to build modular software capabilities and from heterogeneous service marketplaces and locations [37] [221] [222] [223] [225]; g) enabling reusability of small, atomic, reusable components through decomposition [252] [252] [29]; and h) guiding atomic service discovery, selection, and complex services prototyping and composition in the cloud environments [252] [41] [40]. Figure 12 summarizes the motivations above.…”

Section: Data Training and Compositionmentioning

confidence: 99%

IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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As billions of IoT devices join the Internet, researchers and innovators increasingly explore IoT capabilities achieved via service composition or reuse of existing capabilities via service decomposition. Many systematic literature reviews (SLRs) were produced on this subject; however, two issues remain to be addressed: i) a reference taxonomy of the different aspects of IoT capabilities composition and decomposition is needed, and ii) many formal questions (e.g., standards role, formal representations applications, state-space explosion countermeasures, etc.), technical questions (e.g., composition process types and automation levels synergies, service decomposition categories, the role of AI/ML, etc.), and QoS questions (e.g., privacy, interoperability, and scalability challenges and solutions, etc.) remain unanswered. We introduce this work by discussing notions of IoT capabilities composition and decomposition in a layered IoT architecture while highlighting the strengths and weaknesses of existing SLRs. We identify unanswered questions through gaps in related work and motivate these questions using the PICOC methodology. We explain the search methodology and organize the topic questions using the proposed reference taxonomy. The identified research questions are answered, and trends and gaps that need additional attention from the research community are highlighted. This effort benefits city planners and end-users of IoT systems as it contributes to a better understanding of the role of composition and decomposition of IoT capabilities in building valueadded services or reusing existing ones for resource optimization. For researchers, this effort contributes a reference taxonomy for the topic and sheds light on important questions while highlighting corresponding trends and gaps requiring further attention.

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Section: Data Training and Compositionmentioning

confidence: 99%

IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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“…It has also contributed to resolve the well-known challenge of integrating formal methods into the existing software processes (see for example Abrial [7] (p. 766) and Alagar et al [8] (Chapter 2)). The second group comprises works pertaining to improve, transform or extend the syntax of the graphical model with, for instance, text-based notations such as XML to facilitate model construction and/or transformation [9][10][11][12]. The last category includes works addressing the concept of refinement, which is very poorly integrated in semi-formal techniques [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

An Improved User Requirements Notation (URN) Models’ Construction Approach

Dongmo

Poll

2023

Systems

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Semi-formal software techniques have been very successful in industry, government institutions and other areas such as academia. Arguably, they owe a large part of their success to their graphical notation, which is more human-oriented than their counterpart text-based and formal notation techniques. However, ensuring the consistency between two or more models is one of the known challenges of these techniques. This work looks closely at the specific case of the User Requirements Notation (URN) technique. Although the abstract model of URN provides for link elements to ensure the consistency between its two main components, namely, Goal-Oriented Requirement Language (GRL) and Use Case Maps (UCM), the effective implementation of such links is yet to be fully addressed. This paper performs a detailed analysis of the existing URN models construction process and proposes an improved process with some guidelines to ensure, by construction, the correctness and consistency of the GRL and UCM models. A case study is used throughout the paper to illustrate the suggested solution.

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XMI-based conversion of SysML-STM models to C++ code

Miao,

Zhou,

et al. 2024

Proceedings of the 2024 16th International Conference on Computer Modeling and Simulation

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A framework for mapping UML class into XML data based on technical Specifications

Cited by 3 publications

References 7 publications

IoT Capabilities Composition and Decomposition: A Systematic Review

IoT Capabilities Composition and Decomposition: A Systematic Review

An Improved User Requirements Notation (URN) Models’ Construction Approach

XMI-based conversion of SysML-STM models to C++ code

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