We consider approximate join processing over data streams when memory limitations cause incoming tuples to overflow the available space, precluding exact processing. Selective evi...
Mining frequent patterns on streaming data is a new challenging problem for the data mining community since data arrives sequentially in the form of continuous rapid streams. In t...
This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. The ST...
Rajeev Motwani, Jennifer Widom, Arvind Arasu, Bria...
Mining frequent itemsets from data streams has proved to be very difficult because of computational complexity and the need for real-time response. In this paper, we introduce a no...
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of many problems in data stream analysis. It has been observed that several propose...
Radu Berinde, Graham Cormode, Piotr Indyk, Martin ...