Recently, big data streams have become ubiquitous due to the fact that a number of
applications generate a huge amount of data at a great velocity. This made it difficult for …

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 …

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

We develop a framework for efficiently transforming certain approximation algorithms into
differentially-private variants, in a black-box manner. Specifically, our results focus on …

The main bottleneck in designing efficient dynamic algorithms is the unknown nature of the
update sequence. In particular, there are some problems, like 3-vertex connectivity, planar …

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 …

The main bottleneck in designing efficient dynamic algorithms is the unknown nature of the
update sequence. In particular, there are problems where the separation in runtime between …

Large-scale matrix computation becomes essential for many data data applications, and
hence the problem of sketching matrix with small space and high precision has received …

Stream frequency measurements are fundamental in many data stream applications such as
financial data trackers, intrusion-detection systems, and network monitoring. Typically …