Finding Hierarchical Heavy Hitters in Data Streams

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Federated Heavy Hitters Discovery with Differential Privacy (2019, background): “The idea of using trie-like structures for ﬁnding frequent sequences in data streams has been explored before in (Cormode et al., 2003; Bassily et al., 2017).”
Practical verified computation with streaming interactive proofs (2011, methodology): “We will specify a witness set W of size O(φ−1) to identify to identify all leaves j with fj > T ; we base W on the concept of Hierarchical Heavy Hitters (HHHs) [37].”
Efficient Computation of Frequent and Top-k Elements in Data Streams (2005, methodology): “Currently, data stream management systems are used for monitoring click streams [31], stock tickers [11, 46], sensor readings [7], telephone call records [15], network packet traces [17], auction bidding patterns [2], traffic management [3], network-aware clustering [12], and security against DoS [1”
An improved data stream summary: the count-min sketch and its applications (2004, methodology): “Using our CM sketch, the cost of the solution given in [7] for the turnstile model can be improved from O( h ε(2) log 1 δ ) space and time per update to O( ε log 1 δ ) space and O(h log 1 δ ) time per update.”
Data streams: algorithms and applications (2005, unknown): “A somewhat related problem concerns multidimensional signals.”