AIM: Approximate intelligent matching for time series data

Edward D. Kim, Joyce M.W. Lam, Jiawei Han

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


Time-series data mining presents many challenges due to the intrinsic large scale and high dimensionality of the data sets. Sub-sequence similarity matching has been an active research area driven by the need to analyse large data sets in the financial, biomedical and scientific databases. In this paper, we investigate an intelligent subsequence similarity matching of time series queries based on effcient graph traversal. We introduce a new problem, the approximate partial matching of a query sequence in a time series database. Our system can address such queries with high specificity and minimal time and space overhead. The performance bottleneck of the current methods were analysed and we show our method can improve the performance of the time series queries significantly. It is general and exible enough to find the best approximate match query without specifying a tolerance " parameter.

Original languageEnglish (US)
Title of host publicationData Warehousing and Knowledge Discovery - 2nd International Conference, DaWaK 2000, Proceedings
EditorsYahiko Kambayashi, Mukesh Mohania, A. Min Tjoa
Number of pages11
ISBN (Print)3540679804, 9783540679806
StatePublished - 2000
Externally publishedYes
Event2nd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2000 - London, United Kingdom
Duration: Sep 4 2000Sep 6 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2000
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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