We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
interpretation. In the context of stream parallelism, this analysis identi es an amount of input data for which predicate execution can safely wait without danger of introducing de...
Moreno Falaschi, Patrick Hicks, William H. Winsbor...
: The generic problem of estimation and inference given a sequence of i.i.d. samples has been extensively studied in the statistics, property testing, and learning communities. A n...
The primary constraint in the effective mining of data streams is the large volume of data which must be processed in real time. In many cases, it is desirable to store a summary...
A simple, tunable, synthetic benchmark with a performance directly related to applications would be of great benefit to the scientific computing community. In this paper, we prese...