A high-performance algorithm for identifying frequent items in data streams
D Anderson, P Bevan, K Lang, E Liberty… - Proceedings of the …, 2017 - dl.acm.org
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 …
data measurement and analysis. Misra and Gries introduced their seminal algorithm for the …
Parallel space saving on multi‐and many‐core processors
Given an array of n elements and a value 2≤ k≤ n, a frequent item or k‐majority element is
an element occurring in more than n/k times. The k‐majority problem requires finding all of …
an element occurring in more than n/k times. The k‐majority problem requires finding all of …
On frequency estimation and detection of frequent items in time faded streams
We deal with the problem of detecting frequent items in a stream under the constraint that
items are weighted, and recent items must be weighted more than older ones. This kind of …
items are weighted, and recent items must be weighted more than older ones. This kind of …
Fast and accurate mining of correlated heavy hitters
The problem of mining correlated heavy hitters (CHH) from a two-dimensional data stream
has been introduced recently, and a deterministic algorithm based on the use of the Misra …
has been introduced recently, and a deterministic algorithm based on the use of the Misra …
CMSS: Sketching based reliable tracking of large network flows
Reliably tracking large network flows in order to determine so-called elephant flows, also
known as heavy hitters or frequent items, is a common data mining task. Indeed, this kind of …
known as heavy hitters or frequent items, is a common data mining task. Indeed, this kind of …
Mining frequent items in unstructured P2P networks
Large scale decentralized systems, such as P2P, sensor or IoT device networks are
becoming increasingly common, and require robust protocols to address the challenges …
becoming increasingly common, and require robust protocols to address the challenges …
On frequency estimation and detection of heavy hitters in data streams
A stream can be thought of as a very large set of data, sometimes even infinite, which arrives
sequentially and must be processed without the possibility of being stored. In fact, the …
sequentially and must be processed without the possibility of being stored. In fact, the …
Data stream fusion for accurate quantile tracking and analysis
UDDSketch is a recent algorithm for accurate tracking of quantiles in data streams, derived
from the DDSketch algorithm. UDDSketch provides accuracy guarantees covering the full …
from the DDSketch algorithm. UDDSketch provides accuracy guarantees covering the full …
Parallel mining of time-faded heavy hitters
In this paper we present PFDCMSS (Parallel Forward Decay Count–Min Space Saving)
which, to the best of our knowledge, is the world first message–passing parallel algorithm for …
which, to the best of our knowledge, is the world first message–passing parallel algorithm for …
Deterministic, Fast and Accurate Solution of the Heavy Hitters q-Tail Latencies Problem
The heavy hitters-tail latencies problem has been introduced recently. This problem, framed
in the context of data stream monitoring, requires approximating the quantiles of the heavy …
in the context of data stream monitoring, requires approximating the quantiles of the heavy …