STAGGER: Periodicity mining of data streams using expanding sliding windows

Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagarmid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

Sensor devices are becoming ubiquitous, especially in measurement and monitoring applications. Because of the real-time, append-only and semi-infinite natures of the generated sensor data streams, an online incremental approach is a necessity for mining stream data types. In this paper, we propose STAGGER: a one-pass, online and incremental algorithm for mining periodic patterns in data streams. STAGGER does not require that the user pre-specify the periodicity rate of the data. Instead, STAGGER discovers the potential periodicity rates. STAGGER maintains multiple expanding sliding windows staggered over the stream, where computations are shared among the multiple overlapping windows. Small-length sliding windows are imperative for early and real-time output, yet are limited to discover short periodicity rates. As streamed data arrives continuously, the sliding windows expand in length in order to cover the whole stream. Larger-length sliding windows are able to discover longer periodicity rates. STAGGER incrementally maintains a tree-like data structure for the frequent periodic patterns of each discovered potential periodicity rate. In contrast to the Fourier/Wavelet-based approaches used for discovering periodicity rates, STAGGER not only discovers a wider, more accurate set of periodicities, but also discovers the periodic patterns themselves. In fact, experimental results with real and synthetic data sets show that STAGGER outperforms Fourier/Wavelet-based approaches by an order of magnitude in terms of the accuracy of the discovered periodicity rates. Moreover, real-data experiments demonstrate the practicality of the discovered periodic patterns.

Original languageEnglish
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Pages188-199
Number of pages12
DOIs
Publication statusPublished - 1 Dec 2006
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other6th International Conference on Data Mining, ICDM 2006
CountryChina
CityHong Kong
Period18/12/0622/12/06

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Elfeky, M. G., Aref, W. G., & Elmagarmid, A. K. (2006). STAGGER: Periodicity mining of data streams using expanding sliding windows. In Proceedings - Sixth International Conference on Data Mining, ICDM 2006 (pp. 188-199). [4053047] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2006.153