We consider the problem of building relational probabilistic models with an underlying ontology that defines the classes and properties used in the model. Properties in the ontology form random variables when applied to individuals. When an individual is not in the domain of a property, the corresponding random variable is undefined. If we are uncertain about the types of individuals, we may be...

Clustering is an unsupervised classification technique for grouping set of abstract objects into classes of similar objects. Clustering uncertain data is one of the essential tasks in mining uncertain data. Uncertain data is typically found in the area of sensor networks, weather data, customer rating data etc. The earlier methods for clustering uncertain data based on probability distribution,...

Today, a large amount of uncertain data is produced by several applications where the management systems of traditional databases including indexing methods are not suitable to handle such type of data. In this paper, we propose an inverted based index method for efficiently searching uncertain categorical data over distributed environments. We address two kinds of query over the distributed un...

A graph containing some edges with probability measures and other uncertain is referred to as an random graph. Numerous real-world problems in social networks transportation can be boiled down optimization graphs. Actually, information graphs always asymmetric. Regularization a common problem theory, the regularity index fundamentally measurable indicator of Therefore, this paper investigates w...

This paper proposes a robust optimal eco-driving control strategy considering multiple signalized intersections with uncertain traffic signal timing. A spatial vehicle velocity profile optimization formulation is developed to minimize the global fuel consumption, with driving time as one state variable. We introduce the concept of effective red-light duration (ERD), formulated as a random varia...

multistage stochastic programming is a key technology for making decisions over time in an uncertain environment. one of the promising areas in which this technology is implementable, is medium term planning of electricity production and trading where decision makers are typically faced with uncertain parameters (such as future demands and market prices) that can be described by stochastic proc...

Uncertainty theory is a new branch of axiomatic mathematics for studying the subjective uncertainty. In uncertain theory, uncertain variable is a fundamental concept, which is used to represent imprecise quantities (unknown constants and unsharp concepts). Entropy of uncertain variable is an important concept in calculating uncertainty associated with imprecise quantities. This paper introduces...

For the Vlasov-Poisson equation with random uncertain initial data, we prove that the Landau damping solution given by the deterministic counterpart (Caglioti and Maffei, J. Stat. Phys., 92:301-323, 1998) depends smoothly on the random variable if the time asymptotic profile does, under the smoothness and smallness assumptions similar to the deterministic case. The main idea is to generalize th...