With the evolution of new technologies, the production of digital data is constantly growing. It
is thus necessary to develop data management strategies in order to handle the large-scale …

Every industry has significant data output as a product of their working process, and with the
recent advent of big data mining and integrated data warehousing it is the case for a robust …

A large class of data repair algorithms rely on integrity constraints to detect and repair errors.
A well-studied class of constraints is Functional Dependencies (FDs, for short). Although …

Incomplete information often occurs along with many database applications, eg, in data
integration, data cleaning, or data exchange. The idea of data imputation is often to fill the …

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 paper explores the data cleaning challenges that arise in using WiFi connectivity data
to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi …

For organizations data quality is a prerequisite for automated decision making and agility. To
provide high quality data, numerous tools have emerged that support the different steps of …

The Internet of Things (IoT) has rapidly transitioned from a novelty to a common, and often
critical, part of residential, business, and industrial environments. Security vulnerabilities and …

With the emergence of machine learning (ML) techniques in database research, ML has
already proved a tremendous potential to dramatically impact the foundations, algorithms …

With technological innovations, enterprises in the real world are managing every iota of data
as it can be mined to derive business intelligence (BI). However, when data comes from …