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

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 …

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 …

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 …

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 …

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 …

This paper presents the design of a read-optimized relational DBMS that contrasts sharply
with most current systems, which are write-optimized. Among the many differences in its …

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

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 …