In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …

In many real-world applications, information such as web click data, stock ticker data, sensor
network data, phone call records, and traffic monitoring data appear in the form of data …

Data collection has become a ubiquitous function of large organizations {not only for record
keeping, but to support a variety of data analysis tasks that are critical to the organizational …

Monitoring tasks, such as anomaly and DDoS detection, require identifying frequent flow
aggregates based on common IP prefixes. These are known as hierarchical heavy hitters …

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 proposed …

Data items that arrive online as streams typically have attributes which take values from one
or more hierarchies (time and geographic location, source and destination IP addresses …

Cloud operators require real-time identification of Heavy Hitters (HH) and Hierarchical
Heavy Hitters (HHH) for applications such as load balancing, traffic engineering, and attack …

Abstract The Hierarchical Heavy Hitters problem extends the notion of frequent items to data
arranged in a hierarchy. This problem has applications to network traffic monitoring …

Estimating frequencies of items over data streams is a common building block in streaming
data measurement and analysis. Misra and Gries introduced their seminal algorithm for the …

We give the first optimal bounds for returning the ℓ1-heavy hitters in a data stream of
insertions, together with their approximate frequencies, closing a long line of work on this …