We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data

Across saccades, blinks, blank screens, movie cuts, and other interruptions, ob-servers fail to detect substantial changes to the visual details of objects and scenes. This inability to spot changes (“change blindness”) is the focus of this special issue of Visual Cognition. This introductory paper briefly reviews recent studies of change blindness, noting the relation of these findings to earlier re-search and discussing the inferences we can draw from them. Most explanations of change blindness assume that we fail to detect changes because the changed display masks or overwrites the initial display. Here I draw a distinction between intentional and incidental change detection tasks and consider how alternatives to the “overwriting ” explanation may provide better explanations for change blindness. Imagine you are watching a movie in which an actor is sitting in a cafeteria with a jacket slung over his shoulder. The camera then cuts to a close-up and his jacket is now over the back of his chair. You might think that everyone would notice this obvious editing mistake. Yet, recent research on visual memory has found that people are surprisingly poor at noticing large changes to objects, photographs, and motion pictures from one instant to the next (see Simons & Levin, 1997 for a review). Although researchers have long noted the existence

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous

Most current approaches to concurrency control in database systems rely on locking of data objects as a control mechanism. In this paper, two families of nonlocking concurrency controls are presented. The methods used are “optimistic ” in the sense that they rely mainly on transaction backup as a

Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison

, specialized panoramic cameras or by "stitching" together overlapping photographs taken with a regular camera. Walking in a space is currently accomplished by "hopping" to different panoramic points. The image-based approach has been used in the commercial product QuickTime VR, a virtual

-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case- based reasoning, describes some of the leading methodo- logical approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined

Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been

for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels. We demonstrate our algorithm on object recognition tasks and show it to be accurate and dramatically faster than current approaches.

– Piecewise interpolation methods are fast, but only C n for "small " n, n ∈ {0, 1} for surfaces – Efficient trigonometric interpolation methods (using the FFT) are C ∞ but require evenly spaced data