Mining approximate top-K subspace anomalies in multi-dimensional time-series data

Xiaolei Li, Jiawei Han

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

Abstract

Market analysis is a representative data analysis process with many applications. In such an analysis, critical numerical measures, such as profit and sales, fluctuate over time and form time-series data. Moreover, the time series data correspond to market segments, which are described by a set of attributes, such as age, gender, education, income level, and product-category, that form a multi-dimensional structure. To better understand market dynamics and predict future trends, it is crucial to study the dynamics of time-series in multi-dimensional market segments. This is a topic that has been largely ignored in time series and data cube research. In this study, we examine the issues of anomaly detection in multi-dimensional time-series data. We propose timeseries data cube to capture the multi-dimensional space formed by the attribute structure. This facilitates the detection of anomalies based on expected values derived from higher level, "more general" time-series. Anomaly detection in a time-series data cube poses computational challenges, especially for high-dimensional, large data sets. To this end, we also propose an efficient search algorithm to iteratively select subspaces in the original high-dimensional space and detect anomalies within each one. Our experiments with both synthetic and real-world data demonstrate the effectiveness and efficiency of the proposed solution.

Original languageEnglish (US)
Title of host publication33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
EditorsJohannes Gehrke, Christoph Koch, Minos Garofalakis, Karl Aberer, Carl-Christian Kanne, Erich J. Neuhold, Venkatesh Ganti, Wolfgang Klas, Chee-Yong Chan, Divesh Srivastava, Dana Florescu, Anand Deshpande
PublisherAssociation for Computing Machinery, Inc
Pages447-458
Number of pages12
ISBN (Electronic)9781595936493
StatePublished - 2007
Event33rd International Conference on Very Large Data Bases, VLDB 2007 - Vienna, Austria
Duration: Sep 23 2007Sep 27 2007

Publication series

Name33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings

Other

Other33rd International Conference on Very Large Data Bases, VLDB 2007
CountryAustria
CityVienna
Period9/23/079/27/07

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems
  • Software

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