In pursuit of outliers in multi-dimensional data streams

Shiblee Sadik, Le Gruenwald, Eleazar Leal

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

3 Scopus citations

Abstract

Among many Big Data applications are those that deal with data streams. A data stream is a sequence of data points with timestamps that possesses the properties of transiency, infiniteness, uncertainty, concept drift, and multi-dimensionality. In this paper we propose an outlier detection technique called Orion that addresses all the characteristics of data streams. Orion looks for a projected dimension of multi-dimensional data points with the help of an evolutionary algorithm, and identifies a data point as an outlier if it resides in a low-density region in that dimension. Experiments comparing Orion with existing techniques using both real and synthetic datasets show that Orion achieves an average of 7X the precision, 5X the recall, and a competitive execution time compared to existing techniques.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages512-521
Number of pages10
ISBN (Electronic)9781467390040
DOIs
StatePublished - Jan 1 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period12/5/1612/8/16

Keywords

  • Data Mining
  • Data Streams
  • Outlier Detection

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