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In this paper, we propose a formal model and a platform for software change management. The model is based on graphs rewriting, and deal with both multi-language source codes and heterogeneous database schemas. These are represented by software components linked by meaningful relationships. The change impact analysis is done, using a Knowledge-Based System, that includes impact propagation rules preserving the software consistency. This is implemented by an integrated platform including a multilanguage parsing tool, and a software change management module.

Change management is a critical task to control the side effects of a modification during the business process evolution. The evolution of business processes is an essential activity for the companies to better fulfill the requirements of their customers and different stakeholders. In this respect, the enterprises should adopt an effective mechanism in order to achieve the flexible business process models. It is important to identify and highlight the ripple effects of a change for minimizing their impact on other parts or entities of the system and associated services. This paper proposes a dependency-centric approach for change impact analysis. We attempt to demonstrate the change impact propagation in business process models by detecting and analyzing the interdependencies among all parts of business processes along with associated services. It can support the maintenance and evolution of business process models. The major objective is to help the modelers and business experts to assess the associated risk of intended changes and estimate the effort required for their accomplishments.

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The business process models are often subjected to change rapidly in order to cope with the market demands. It may be useful for companies to adapt a monitoring mechanism to achieve flexible business process models. It is also desirable to control the ripple effects of a change on whole or part of the business process and its running instances. It requires an exhaustive understanding of concerned changes and their application levels. In this article, we propose a methodology based on dependency analysis for an a priori change impact analysis in the business process models. The approach is based on the ontology definitions to describe the dependency relationships. The major objective is to obtain a knowledge base to help the designers and business experts to estimate the associated risk of intended changes and the effort required for their implementation.

Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such “industrial big data” can relay its usefulness to reach further utilitarian applications. In this context, Machine Learning (ML) is among the major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes.

The Logistic processes generally lead to complex physical flows dealing with various logistic elements. It has been widely observed that the quality of uncontrolled processes decline with the evolving complexity. It may make them incoherent and quasi obsoletes. Thus, the continuous optimization of logistic processes is essential for the consistent continuity of logistic activities, and henceforth, it supports their desired growth. In this paper, we propose a reasoning system that uses the conceptual domain of logistics and their optimization. The proposed approach is mainly based on the definition of logistic terminologies using ontology. We intend, that a logistic expert may use defined terms to specify a problem. These can be matched to extract the involved logistic processes. It may assist a logistics expert to identify and/or precisely specify the logistic problem. Furthermore, it may identify the logistic processes, that can be executed, to resolve the problem and consequently to resolve the inherent optimization problems. We have been experimenting the different solutions of the Passenger Transportation Problem and eventually built a software framework (based on the composition of web services), to semiautomatically assist the resolution procedure of identified optimization problems.

 +33 644779907 is a self-explainable AutoML system in the form of a Pythonpackage. The system proposes a transparent and justified analysis to discover the most suitable model for optimal performance among multiple ML models. It attempts to automate the process of the algorithms selection, the tunning of hyperparameters, and traceability in supervised ML. AMLBID package Key concepts ContextAutomated Machine Learning (AutoML) Auto ML is often used to help domain experts, who typically have limited ML expertise, in order to generate and build high quality models to better meet their specific business needs.