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...
This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high–cardinality, time–changing data streams. Within the Superv...
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
We investigate the problem of maintaining a topology with small degree as well as small diameter in a dynamic distributed system such that the system always stays connected and pr...
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...