In this paper, we give a simple scheme for identifying approximate frequent items over a sliding window of size n. Our scheme is deterministic and does not make any assumption on ...
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...
Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
Philipp Kranen, Ira Assent, Corinna Baldauf, Thoma...
Recent years have seen growing interest in effective algorithms for summarizing and querying massive, high-speed data streams. Randomized sketch synopses provide accurate approxima...
Graham Cormode, Minos N. Garofalakis, Dimitris Sac...
Abstract. Massive data streams of positional updates become increasingly difficult to manage under limited memory resources, especially in terms of providing near real-time respons...
Michalis Potamias, Kostas Patroumpas, Timos K. Sel...