Abstract
Business processes decomposition and also smart awareness are the two major emerging trends regarding future business values. Effecting factors on the complexity of decision models in Internet of Things (IoT) ecosystems such as nonlinearity of objective functions, discretization of problem solving space, problem size, and also limited resources resulted many recent precise methods unable to find the optimal answer in a reasonable time. Thus, given the NP-hard (nondeterministic polynomial) of the enterprise service composition (ESC) problem, the key issue would be to find a highly improved (nearly optimal) response among the candidate enterprise services by an admirable manner. In fact, depending on the type of issue, we try to improve competency. Creating an improved model of ESC in IoT context may play an effective role in supporting and preferably meeting the needs of an organization with respect to enterprise business processes. This model in turn would allow tailoring the right selection of enterprise services to meet the needs of end users. To manage the complex needs of end users in an enterprise, enterprise services are combined into different models to satisfy end user’s requirements. Usually an atomic service can not address the entire issue of complex requirements, so atomic services have to be composed by an ESC procedure. The ESC ends up reducing time and increasing user satisfaction in addition to improve a few other Quality of Services (QoS). This comparative survey enables enterprise architecture domains (especially enterprise application architecture) to model improved ESC in IoT context despite existing restrictions to achieve admirable value.
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Notes
The Open Group Architecture Framework (TOGAF).
Application architecture supported by The Open Group Architecture Framework (TOGAF) Standard is a blueprint for the individual applications to be deployed, their interactions, and their relationships to the core business processes of the organization.
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Safaei, A., Nassiri, R. & Rahmani, A.M. Enterprise service composition models in IoT context: solutions comparison. J Supercomput 78, 2015–2042 (2022). https://doi.org/10.1007/s11227-021-03873-7
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DOI: https://doi.org/10.1007/s11227-021-03873-7