According to Knight, uncertainty signifies deviations from the expected states, which prevent us from the use of any probability for the determination of a result for a given action or decision [1]. This paper describes the phenomenon of uncertainty in the face of technological megatrends and challenges associated with them. The article focuses on the analysis of the uncertainty in one of the most important technology trends – the Internet of Things (IoT) – on the example of Healthcare. The right decisions are not always equivalent to good results. Sometimes, the decision taken in accordance with general rules brings worse results than the one who breaks them. Such a situation is possible as a result of the uncertainty accompanying the predictions of the future. In this article the concept of the IoT is treated as a big, complex, dynamic system with specific characteristics, dimensions. structures and behaviors. The aim of the article is to analyze the factors that may determine the uncertainty and ambiguity of such systems in the context of the development of Healthcare, and recommendations are made for future research directions.

Nowadays, the business environment is more and more evolved. It has become crucial in such a dynamic and an evolving environment to adopt an approach that is based on a paradigm called "design by reuse" in order to make the process of a given organization reusable. The idea of reuse of the model has emerged with the need to reduce the cost of development and design time. A new concept is born with this configuration; it's the concept of uncertainty since the configurable model means the restriction of the behavior represented by an existing model of the process so that it allows only the desired behavior of the model while all the unwanted behavior is eliminated from it. So, let's choose a path among many, but which one? In this paper, we will propose an entropy-based uncertainty measure based on entropy that enables us to take into account the dynamic aspect of the model at the design-time and to quantify this uncertainty in order to evaluate predictability and managerial efficiency of process models.

The ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant approach able to achieve automatic enrichment from a textual corpus or dataset of various topics. In this paper, we describe a new approach for automatic learning of terminological ontologies from textual corpus based on probabilistic models. In our approach, two topic modeling algorithms are explored, namely LDA and pLSA for learning topic ontology. The objective is to capture semantic relationships between word-topic and topic-document in terms of probability distributions to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and retrieving corresponding topic ontology for a user query demonstrates the effectiveness of the proposed approach.