This work addresses the need for stateful dataflow programs that can rapidly sift through huge, evolving data sets. These data-intensive applications perform complex multi-step c...
Dionysios Logothetis, Christopher Olston, Benjamin...
There is growing interest in run-time detection as parallel and distributed systems grow larger and more complex. This work targets run-time analysis of complex, interactive scien...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Recent innovations have resulted in a plethora of social applications on the Web, such as blogs, social networks, and community photo and video sharing applications. Such applicat...
As the size of available datasets in various domains is growing rapidly, there is an increasing need for scaling data mining implementations. Coupled with the current trends in co...