Today's data centers offer tremendous aggregate bandwidth to clusters of tens of thousands of machines. However, because of limited port densities in even the highest-end swi...
Mohammad Al-Fares, Sivasankar Radhakrishnan, Barat...
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
The dimensionality reduction problem has been widely studied in the database literature because of its application for concise data representation in a variety of database applica...
Map-reduce framework has received a significant attention and is being used for programming both large-scale clusters and multi-core systems. While the high productivity aspect of ...