Stream Frequency over Interval Queries

Ran Ben Basat, Roy Friedman, Rana Shahout

Research output: Contribution to journalArticlepeer-review


Stream frequency measurements are fundamental in many data stream applications such as financial data trackers, intrusion-detection systems, and network monitoring. Typically, recent data items are more relevant than old ones, a notion we can capture through a sliding window abstraction. This paper considers a generalized sliding window model that supports stream frequency queries over an interval given at query time. This enables drill-down queries, in which we can examine the behavior of the system in finer and finer granularities. For this model, we asymptotically improve the space bounds of existing work, reduce the update and query time to a constant, and provide deterministic solutions. When evaluated over real Internet packet traces, our fastest algorithm processes items 90{250 times faster, serves queries at least 730 times quicker and consumes at least 40% less space than the best known method.

Original languageEnglish
Pages (from-to)433-445
Number of pages13
JournalProceedings of the VLDB Endowment
Issue number4
StatePublished - Dec 2018
Event45th International Conference on Very Large Data Bases, VLDB 2019 - Los Angeles, United States
Duration: 26 Aug 201730 Aug 2017

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)


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