Entity resolution (ER), also known as duplicate detection or record matching, is the problem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of ER for uncertain data, which we call ERUD. We propose two different approaches for the ERUD problem based on two classes of similarity functions, i.e. context-free and context-sensitive. We prop...

Analyzing uncertain databases is a challenge in data mining research. Usually, data mining methods rely on precise values. In scenarios where uncertain values occur, e.g. due to noisy sensor readings, these algorithms cannot deliver highquality patterns. Beside uncertainty, data mining methods face another problem: high dimensional data. For finding object groupings with locally relevant dimens...

Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. In this paper, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic...

Fitting of uncertain data, that is, fitting of data points that are subject to some error, has important applications for example in statistics and for the evaluation of results from physical experiments. Fitting in these problem domains is usually achieved with polynomial approximation, which involves the minimization of an error at discrete data points. Norms typically used for this minimizat...

abbreviate title: " ABC " classification with uncertain data 2 " ABC " CLASSIFICATION WITH UNCERTAIN DATA. This study presents an alternative way of classifying the different productive items of a company. A fuzzy model for the magnitudes involved (demand and cost) is described. This model contrasts with the classic Pareto classification (ABC), which ranks productive items according to their im...

An important quality of sensor data is that it is often uncertain or imprecise. This uncertainty can be an inherent aspect of the data (e.g. due to known errors in the measuring device, such as the Gaussian error in GPS readings), or it may be introduced in order to achieve scalability [2, 1], or to ensure a certain level of privacy [4]. Existing database management systems provide virtually no...

Data integration systems offer uniform access to a set of autonomous and heterogeneous data sources. An important task in setting up a data integration system is to match the attributes of the source schemas. In this paper, we propose a data integration system which uses the knowledge implied within functional dependencies for matching the source schemas. We build our system on a probabilistic ...

Numerous real-life applications are continually generating huge amounts of uncertain data (e.g., sensor or RFID readings). As a result, top-k queries that return only the k most promising probabilistic tuples become an important means to monitor and analyze such data. These “top” tuples should have both high scores in term of some ranking function, and high occurrence probability. The previous ...

Motivated by sensor networks, mobility data, biology and life sciences, the area of mining uncertain data has recently received a great deal of attention. While various papers have focused on efficiently mining frequent patterns from uncertain data, the problem of discovering a small set of interesting patterns that provide an accurate and condensed description of a probabilistic database is st...

There has been considerable past work studying data integration and uncertain data in isolation. We develop the foundations for local-as-view (LAV) data integration when the sources being integrated are uncertain. We motivate two distinct settings for uncertain-data integration. We then define containment of uncertain databases in these settings, which allows us to express uncertain sources as ...