Distributed top-k monitoring

By Brian Babcock, Christopher Olston. ACM SIGMOD Conference, 2003

485 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Efficient Computation of Frequent and Top-k Elements in Data Streams (2005, background): “Problems studied in this context include approximate frequency moments [1], differences [20], distinct values estimation [22, 33, 45], bit counting [16], duplicate detection [42], approximate quantiles [28, 38, 40], histograms [30, 29], wavelet based aggregate queries [25, 41], correlated aggregate ”
What's hot and what's not: tracking most frequent items dynamically (2003, background): “This should be contrasted to other approaches [4], which also compute the overall hot items from multiple sources, but keeps a large amount of space at each location: instead the focus is on minimizing the amount of communication.”
When things matter: A survey on data-centric internet of things (2016, background): “The general problem of monitoring top-k values from distributed data streams is investigated in Babcock and Olston (2003).”