ions and Techniques for Programming with Uncertain Data

An important step in reliability evaluation of any system is selection of an appropriate mathematical representation. One of the possible mathematical representations is structure function that expresses dependency of system state on states of its components. This function must be completely specified for reliability evaluation of the analyzed system. The structure function is constructed based...

Objective Uncertainty regarding the location of disease acquisition, as well as selective identification of cases, may bias maps of risk. We propose an extension to a distance-based mapping method (DBM) that incorporates weighted locations to adjust for these biases. We demonstrate this method by mapping potential drug-resistant tuberculosis (DRTB) transmission hotspots using programmatic data ...

Frequent pattern mining is the method of finding patterns like itemsets, subsequences and substructures that repeatedly occur in a dataset. In Univariate Uncertain data, each attribute present in a transaction is represented by a quantitative interval and a probability value. U2P-Miner algorithm is used to mine frequent patterns from U2 data. The number of intervals has a great impact on the ti...

The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty—using different uncertainty models—on the data mining results. Our framework ...

The uncertainty associated to stored information can be put in direct correspondence to the extent to which these data violate conditions expressed as semantic integrity constraints. Thus, imposing and checking such constraints provides a better control over uncertain data. We present and discuss a condition which ensures the violation tolerance of methods for integrity checking. Usually, such ...

In recent years, uncertain data has become ubiquitous because of new technologies for collecting data which can only measure and collect the data in an imprecise way. Furthermore, many technologies such as privacy-preserving data mining create data which is inherently uncertain in nature. As a result there is a need for tools and techniques for mining and managing uncertain data. This chapter d...

In this paper, we will propose a technique for multidimensional enhancement of uncertain data. In many applications, the uncertainty in the different dimensions is caused by independent factors, especially if the different dimensions have been collected from independent sources. In such cases, it is possible to enhance the quality of the data and reduce the underlying uncertainty by using multi...

Detecting edges in images which are distorted by unreliable or missing data samples can be done using normalized (differential) convolution. This work presents a comparison between gradient estimation using normalized convolution and gradient estimation using normalized differential convolution with regard to speed, accuracy, and noisesensitivity.