Top-\(k\) Frequent Item Maintenance over Streams

  • Moses CharikarEmail author
Part of the Data-Centric Systems and Applications book series (DCSA)


We consider the problem of finding the most frequent items in a data stream. Given a data stream \(a_{1},a_{2},\ldots,a_{n}\), where each \(a_{i} \in \{1,\ldots,m\}\), we would like to identify the items that occur most frequently in one pass over the data stream using a small amount of storage space. Such problems arise in a variety of settings. For example, a search engine might be interested in gathering statistics about its query stream and in particular, identifying the most popular queries. Another application is to detecting network anomalies by monitoring network traffic. We describe a variety of approaches that have been proposed to solve these problems. Our goal is to give a flavor of the various techniques that have been used in this area.


Data Stream Hash Function Total Count Frequent Item Deterministic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentStanford UniversityStanfordUSA

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