We present STAR, a self-tuning algorithm that adaptively sets numeric precision constraints to accurately and efficiently answer continuous aggregate queries over distributed data...
Navendu Jain, Michael Dahlin, Yin Zhang, Dmitry Ki...
We consider an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data. Signif...
Because it takes time and trust to establish agreement, traditional consensus-based architectural styles cannot safely accommodate resources that change faster than it takes to tr...
Linear DSP kernels such as transforms and filters are comprised exclusively of additions and multiplications by constants. These multiplications may be realized as networks of ad...
We propose a framework for quantitative security analysis of machine learning methods. Key issus of this framework are a formal specification of the deployed learning model and a...