This study addresses the problem of finding the range of efficiency for each Decision Making Unit (DMU) considering uncertain data. Uncertainty in the DMU coefficients in each factor (input or output) is captured through interval coefficients (i.e., these are uncertain but bounded). A twophase additive Data Envelopment Analysis (DEA) model for performance evaluation is used, which is adapted to...

In this text we show how points, point pairs, lines, planes, circles, spheres, and rotation, translation and dilation operators and their uncertainty can be evaluated from uncertain data in a unified manner using the Geometric Algebra of conformal space. This extends previous work by Förstner et al. [3] from points, lines and planes to non-linear entities and operators, while keeping the linear...

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters....

Many recent applications involve processing and analyzing uncertain data. In this paper, we combine the feature of top-k objects with that of skyline to model the problem of top-k skyline objects against uncertain data. The problem of efficiently computing top-k skyline objects on large uncertain datasets is challenging in both computing the top-k skyline objects is developed for discrete cases...

Many frequent pattern mining algorithms find patterns from traditional transaction databases, in which the content of each transaction—namely, items—is definitely known and precise. However, there are many real-life situations in which the content of transactions is uncertain. To deal with these situations, we propose a tree-based mining algorithm to efficiently find frequent patterns from unce...

Current data integration techniques are successful at managing well-defined and wellunderstood data integration tasks, but do not cope well with uncertainty. However, the amount of uncertain data is growing with the number and variety of data sources being integrated, both in traditional data integration tasks s.a. enterprise data integration, and in next generation integration problems, s.a. c...

Reverse topk queries are proposed from the perspective of a product manufacturer, which are essential for manufacturers to assess the potential market. However, the existing approaches for reverse topk queries are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods cannot be applied to pr...

This paper lays out provocative assertions about major challenges for the modeling and simulation community. One relates to building M&S for the purpose of assisting the search for strategies that are flexible, adaptive, and robust despite uncertainty. A key aspect of this search is exploratory analysis, coupled with selective zoom. These, in turn, require multiresolution modeling with sound mo...

Modeling the structure of biological molecules is critical for understanding how these structures perform their function, and for designing compounds to modify or enhance this function (for medicinal or industrial purposes). The determination of molecular structure involves defining three-dimensional positions for each of the constituent atoms using a variety of experimental, theoretical and em...

Determining the optimal scale size of a combined cycle power plant is inherently a complex problem often with multiple and conflicting criteria as well as uncertain factors. The complexity of the problem is compounded by the production of undesirable outputs and the presence of natural and managerial disposability. We propose a customized data envelopment analysis (DEA) method for solving the r...