Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

Abstract In the Internet of Things (IoT), data gathered from a global-scale deployment of
smart-things, are the base for making intelligent decisions and providing services. If data are …

Internet of Things (IoT) has been growing rapidly due to recent advancements in
communications and sensor technologies. Meanwhile, with this revolutionary transformation …

This book is the result of a study path that started in 2006, when the two authors of this book
published the book Data Quality: Concepts, Methodologies and Techniques. After 8 years …

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor
devices, and Web technologies, the Internet of Things (IoT) approach has gained …

The missing values, widely existed in multivariate time series data, hinder the effective data
analysis. Existing time series imputation methods do not make full use of the label …

Advances in sensor data collection technology, such as pervasive and embedded devices,
and RFID Technology have lead to a large number of smart devices which are connected to …

Errors are prevalent in time series data, which is particularly common in the industrial field.
Data with errors could not be stored in the database, which results in the loss of data assets …

Errors are prevalent in time series data, such as GPS trajectories or sensor readings.
Existing methods focus more on anomaly detection but not on repairing the detected …

Today's manufacturing environments are very dynamic and turbulent. Traditional enterprise
information systems (EISs) have mostly been implemented upon hierarchical architectures …