STAGGER: Periodicity mining of data streams using expanding sliding windows

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

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

19 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 - IEEE International Conference on Data Mining, ICDM
Pages188-199
Number of pages12
DOIs
Publication statusPublished - 1 Dec 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Other

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

Fingerprint

Sensors
Data structures
Monitoring
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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

STAGGER : Periodicity mining of data streams using expanding sliding windows. / Elfeky, Mohamed G.; Aref, Walid G.; Elmagarmid, Ahmed.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 188-199 4053047.

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

Elfeky, MG, Aref, WG & Elmagarmid, A 2006, STAGGER: Periodicity mining of data streams using expanding sliding windows. in Proceedings - IEEE International Conference on Data Mining, ICDM., 4053047, pp. 188-199, 6th International Conference on Data Mining, ICDM 2006, Hong Kong, China, 18/12/06. https://doi.org/10.1109/ICDM.2006.153
Elfeky MG, Aref WG, Elmagarmid A. STAGGER: Periodicity mining of data streams using expanding sliding windows. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 188-199. 4053047 https://doi.org/10.1109/ICDM.2006.153
Elfeky, Mohamed G. ; Aref, Walid G. ; Elmagarmid, Ahmed. / STAGGER : Periodicity mining of data streams using expanding sliding windows. Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. pp. 188-199
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