In the adversarially robust streaming model, a stream of elements is presented to an
algorithm and is allowed to depend on the output of the algorithm at earlier times during the …

Clustering is an important technique for identifying structural information in large-scale data
analysis, where the underlying dataset may be too large to store. In many applications …

In the G-sampling problem, the goal is to output an index i of a vector f∈ Rn, such that for all
coordinates j∈[n],[Pr [i= j]=(1±ε)(G (fj))/(∑ k∈[n] G (fk))+ γ,] where G: R→ R≥ 0 is some non …

We study the distinct elements and $\ell_p $-heavy hitters problems in the sliding window
model, where only the most recent $ n $ elements in the data stream form the underlying set …

In this paper we develop streaming algorithms for the diameter problem and the k-center
clustering problem in the sliding window model. In this model we are interested in …

Streaming computation plays an important role in large-scale data analysis. The sliding
window model is a model of streaming computation which also captures the recency of the …

The sliding window model generalizes the standard streaming model and often performs
better in applications where recent data is more important or more accurate than data that …

In the time-decay model for data streams, elements of an underlying data set arrive
sequentially with the recently arrived elements being more important. A common approach …

We present a novel technique for computing a center-based approximation of a drifting
distribution. Given k≥ 1 and a stream of data, whose distribution is changing over time, the …

We initiate the study of numerical linear algebra in the sliding window model, where only the
most recent W updates in the data stream form the underlying set. Although most existing …