Research in data stream algorithms has blossomed since late 90s. The talk will trace the history of the Approximate Frequency Counts paper, how it was conceptualized and how it influenced data stream research. The talk will also touch upon a recent development: analysis of personal data streams for improving our quality of lives.  BIOGRAPHICAL SKETCHES This paper [1], one of many on the hot topic of data streams that year (2002), presents algorithms for computing frequency counts exceeding a user-specified threshold. The paper deftly combines theory, algorithms, and experiments, introducing novel algorithms for sticky sampling and lossy counting (though with provably bounded error), which are important for many applications in databases in general, in data mining, in web-server logs, and in networking. A beautifully written paper, it has garnered a truly amazing number of citations over the last decade (a quality shared by some of the other papers appearing in that unusually impactful conference), including a good number just in the last year, a sign that the paper is still quite relevant. One such general concept highlighted in the paper is that of summary data structures with a small memory footprint.
In REF , the authors propose the Sticky Sampling algorithm and the Lossy Counting algorithm to compute approximate frequency counts of elements in a data stream.