Internet of Measurement Things Architecture: Proof of Concept with Scope of Accreditation.

Many industries, such as manufacturing, aviation, and power generation, employ sensitive measurement devices to be calibrated by certified experts. The diversity and sophistication of measurement devices and their calibration needs require networked and automated solutions. Internet of Measurement Things (IoMT) is an architectural framework that is based on the Industrial Internet of Things for the calibration industry. This architecture involves a layered model with a cloud-centric middle layer. In this article, the realization of this conceptual architecture is described. The applicability of the IoMT architecture in the calibration industry is shown through an editor application for Scope of Accreditation. The cloud side of the implementation is deployed to Microsoft Azure. The editor itself is created as a cloud service, and IoT Hub is used to collect data from calibration laboratories. By adapting the IoMT architecture to a commonly used cloud platform, considerable progress is achieved to encompass Metrology data and serve the majority of the stakeholders.

Clone detection for business process models.

Models are key in software engineering, especially with the rise of model-driven software engineering. One such use of modeling is in business process modeling, where models are used to represent processes in enterprises. As the number of these process models grow in repositories, it leads to an increasing management and maintenance cost. Clone detection is a means that may provide various benefits such as repository management, data prepossessing, filtering, refactoring, and process family detection. In model clone detection, highly similar model fragments are mined from larger model repositories. In this study, we have extended SAMOS (Statistical Analysis of Models) framework for clone detection of business process models. The framework has been developed to support different types of analytics on models, including clone detection. We present the underlying techniques utilized in the framework, as well as our approach in extending the framework. We perform three experimental evaluations to demonstrate the effectiveness of our approach. We first compare our tool against the Apromore toolset for a pairwise model similarity using a synthetic model mutation dataset. As indicated by the results, SAMOS seems to outperform Apromore in the coverage of the metrics in pairwise similarity of models. Later, we do a comparative analysis of the tools on model clone detection using a dataset derived from the SAP Reference Model Collection. In this case, the results show a better precision for Apromore, while a higher recall measure for SAMOS. Finally, we show the additional capabilities of our approach for different model scoping styles through another set of experimental evaluations.