Real-Time Spread Burst Detection in Data Streaming

Haibo Wang, Dimitrios Melissourgos, Chaoyi Ma, Shigang Chen

Research output: Contribution to journalArticlepeer-review


Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-Time burst detection in flow spread. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-Time solution for spread burst detection. It is designed based on a new real-Time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-Time spread estimation, which outperforms the best existing sketches.

Original languageEnglish
Pages (from-to)51-52
Number of pages2
JournalPerformance Evaluation Review
Issue number1
StatePublished - Jun 19 2023

Bibliographical note

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  • data stream
  • real-Time
  • spread burst

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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