We present memory-efficient deterministic algorithms for constructing -nets and -approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic...
Amitabha Bagchi, Amitabh Chaudhary, David Eppstein...
Processing and extracting meaningful knowledge from count data is an important problem in data mining. The volume of data is increasing dramatically as the data is generated by da...
We consider the problem of approximate range counting over streams of d-dimensional points. In the data stream model, the algorithm makes a single scan of the data, which is prese...
Abstract. This text is an informal review of several randomized algorithms that have appeared over the past two decades and have proved instrumental in extracting efficiently quant...
The massive data streams observed in network monitoring, data processing and scientific studies are typically too large to store. For many applications over such data, we must ob...