Data cleaning is naturally framed as probabilistic inference in a generative model of ground-
truth data and likely errors, but the diversity of real-world error patterns and the hardness of …

There is a considerable body of work on data cleaning which employs various principles to
rectify erroneous data and transform a dirty dataset into a cleaner one. One of prevalent …

Data in real world is usually dirty, ie, it may contain inconsistent, noisy, incomplete or
duplicated values. Generally speaking, the identified dimensions of data quality …

Dirty data is ubiquitous in real-world, and data cleaning is a long-standing problem. The
importance of data cleaning is growing in the era of big data. In this paper we propose a …

Data cleaning is naturally framed as probabilistic inference in a generative model of ground-
truth data and likely errors, but the diversity of real-world error patterns and the hardness of …

This chapter is a review of data mining techniques used in medical research. It will cover the
existing applications of these techniques in the identification of diseases, and also present …

Resumen" El presente trabajo tiene como objetivo general evaluar si técnicas del
aprendizaje automático provenientes del área del procesamiento natural del lenguaje …

There is an urgent need for improved models that address the interdependencies between
land use and transportation, and considerable new work is underway to develop such …

Most data cleaning systems aim to go from a given deterministic dirty database to another
deterministic but clean database. Such an enterprise pre-supposes that it is in fact possible …