The dynamically evolving environment of the post-disaster scene—where unpredictable scenarios and uncertain data are commonplace—can bring about considerable complexity into response tasks. The multiplicity and interdependence of approaches to undertaking these tasks may yield many decision alternatives, further complicating the response effort. Additionally, because emergencies are evolving, e...

This work deals with the problem of classifying uncertain data. With this aim the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the concept of nearest neighbor class, rather than on that of nearest neighbor object. The nearest ne...

For classes of simple shortest path problems we analyze the following situation: We are given a graph G = {V, E,w} and are allowed to provide two solutions A and B which are paths from a given starting node to a given target node. Then we learn about randomly perturbed edge lengths ŵ and are allowed to choose the solution from our pair {A,B} that has the better value with respect to these new l...

Key indicators, such as key performance indicators or key compliance indicators are at the heart of modern business intelligence applications. Key indicators are metrics, i.e., numbers, that help an organization to measure and assess how successful it is in reaching predefined goals (e.g., lowering process execution times or increasing compliance with regulations), and typically the people look...

In many application data are imperfect, imprecise or more generally uncertain. Many classification methods have been presented that can handle data in some parts of the learning or the inference process, yet seldom in the whole process. Also, most of the proposed approach still evaluate their results on precisely known data. However, there are no reason to assume the existence of such data in a...

In this extended abstract we apply the notion of skyline to the case of probabilistic relations including correlation among tuples. In particular, we consider the relevant case of the x-relation model, consisting of a set of generation rules specifying the mutual exclusion of tuples. We show how our definitions apply to different ranking semantics and analyze the time complexity for the resolut...

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentations for uncertain data and, for the first time, measure their effects on human decision-making. We show that the use of Natural Language Generation (NLG) improves decision-making under uncertainty, compared to state-of-the...

When Shafer introduced his theory of evidence based on the use of belief functions, he proposed a rule to combine belief functions induced by distinct pieces of evidence. Since then, theoretical justifications of this socalled Dempster’s rule of combination have been produced and the meaning of distinctness has been assessed. We will present practical applications where the fusion of uncertain ...

The motivation of the workgroup was to couple uncertainty management with the notion of change. This encompassed integration, aggregation and mining upon uncertain data. Target questions were measuring uncertainty and defining and measuring the quality of uncertain data. We have left out ambiguousness (in the sense of unclear terms and vagueness) as an orthogonal issue. Uncertainty management i...

Data uncertainty widely exists in many applications. In this paper, we aim at handling top-k queries on uncertain data streams. Since the volume of a data stream is unbounded whereas the memory resource is limited, it is critical to devise one-pass solutions that is both timeand space efficient. In this paper, we use two structures to handle this issue. The DomGraph stores all tuples that are p...