Frequent Pairs in Data Streams: Exploiting Parallelism and Skew

Andrea Campagna, Konstantin Kutzkow, Rasmus Pagh

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

We introduce the Pair Streaming Engine (PairSE) that detects frequent pairs in a data stream of transactions. Our algorithm finds the most frequent pairs with high probability, and gives tight bounds on their frequency. It is particularly space efficient for skewed distribution of pair supports, confirmed for several real-world datasets. Additionally, the algorithm parallelizes easily, which opens up for real-time processing of large transactions. Unlike previous algorithms we make no assumptions on the order of arrival of transactions and pairs. Our algorithm builds upon approaches for frequent items mining in data streams. We show how to efficiently scale these approaches to handle large transactions. We report experimental results showcasing precision and recall of our method. In particular, we find that often our method achieves excellent precision, returning identical upper and lower bounds on the supports of the most frequent pairs.
Original languageEnglish
Title of host publicationProceedings of IEEE International Conference on Data Mining Workshops: ICDMW 2011
PublisherIEEE Computer Society Press
Publication date2011
Pages145 - 150
ISBN (Print)978-1-4673-0005-6
DOIs
Publication statusPublished - 2011

Keywords

  • Frequent pair detection
  • Data stream algorithms
  • Space-efficient computation
  • Parallel processing
  • Skewed distribution handling

Fingerprint

Dive into the research topics of 'Frequent Pairs in Data Streams: Exploiting Parallelism and Skew'. Together they form a unique fingerprint.

Cite this