Abstract. This paper presents a scalable method for parallel symbolic on-the-fly model checking in a distributed memory environment. Our method combines a scheme for on-the-fly mod...
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
We present DEBAR, a scalable and high-performance de-duplication storage system for backup and archiving, to overcome the throughput and scalability limitations of the state-of-th...
Tianming Yang, Hong Jiang, Dan Feng, Zhongying Niu...
Diagnosability of systems is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. Generally, in the l...
Stream computing research is moving from terascale to petascale levels. It aims to rapidly analyze data as it streams in from many sources and make decisions with high speed and a...
Ankur Narang, Vikas Agarwal, Monu Kedia, Vijay K. ...