Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …

With the continuous and vast increase in the amount of data in our digital world, it has been
acknowledged that the number of knowledgeable data scientists can not scale to address …

Recent advances in computer vision-in the form of deep neural networks-have made it
possible to query increasing volumes of video data with high accuracy. However, neural …

Large volumes of videos are continuously recorded from cameras deployed for traffic control
and surveillance with the goal of answering “after the fact” queries: identify video frames with …

Unsupervised Deep Learning (DL) techniques have been widely used in various security-
related anomaly detection applications, owing to the great promise of being able to detect …

Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

Classical anomaly detection is principally concerned with point-based anomalies, those
anomalies that occur at a single point in time. Yet, many real-world anomalies are range …

With the increasing commoditization of computer vision, speech recognition and machine
translation systems and the widespread deployment of learning-based back-end …