Data integration of several sources has gained considerable attentions with the recent popularity of the Web. In the real world, some information may be missing (i.e., partial) and some may be inconsistent from several sources. How to obtain information as complete as possible and detect inconsistency from these sources is thus an interesting question. Most existing work uses a simple graph-bas...

Abstract. Accurate traffic data are the basis for group control of elevators and its performance evaluation by trace driven simulation. The present practice estimates a time series of inter-floor passenger traffic based on commonly available elevator sensor data. The method demands that the sensor data be transformed into sets of passenger input-output data which are consistent in the sense tha...

In ontology-based systems that process data stemming from different sources and that is received over time, as in context-aware systems, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-based query answering....

In concept learning or data mining tasks, the learner is typically faced with a choice of many possible hypotheses characterizing the data. If one can assume that the training data are nois e-free, then the generated hypothesis should be complete and consistent with regard to the data. In real -world problems, however, data are often noisy, and an insistence on full completeness and consistency...

This paper presents an approach to concept learning from inconsistent data that foregoes a solution to the full-blown problem and instead considers a subcase, called bounded inconsistency. Data are said to have bounded inconsistency when some small perturbation to the description of any bad instance will result in a good instance. The key idea to learning in the presence of bounded inconsistenc...

The present research compared the effectiveness of meaningful negation—“That’s wrong”—and simple negation—“No”—to alter automatic prejudice. Participants were trained to negate prejudiceconsistent or prejudice-inconsistent information, using either simple or meaningful negation, and completed an evaluative priming measure of racial prejudice before and after training. No significant changes in ...

We introduce a modular framework for formalizing reasoning with incomplete and inconsistent information. This framework is composed of non-deterministic semantic structures and distance-based considerations. The combination of these two principles leads to a variety of entailment relations that can be used for reasoning about nondeterministic phenomena and are inconsistency-tolerant. We investi...

Shared online databases, such as Google Fusion Tables or Quickbase, allow community members to collaboratively maintain and browse data. While users may believe in conflicting facts (due to conflicting sources, measurements or opinions), current online databases do not offer support for the management of data conflicts. Thus online databases could clearly benefit from technology for uncertain/i...

A Data Warehouse (DW) is a data repository that integrates data from multiple sources and organizes the data according to a set of data structures called dimensions. Each dimension provides a perspective upon which the data can be viewed. In order to support an efficient processing of queries, a dimension is usually required to satisfy different classes of integrity constraints. In this paper, ...

Training a visual inspection device is not straightforward but suffers from the high variation in material to be inspected. This variation causes major difficulties for a human, and this is directly reflected in classifier training. Many inspection devices utilize rule-based classifiers the building and training of which rely mainly on human expertise. While designing such a classifier, a human...