A subsequence matching algorithm supporting moving average transform of arbitrary order in time-series databases using index interpolation

• Published in: Proceedings of the 12th Australasian database conference pp. 37 - 44
• Main Authors: Loh, Woong-Kee, Kim, Sang-Wook
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 29.01.2001
• Series: ACM Other Conferences
• Subjects: Applied computing > Physical sciences and engineering > Mathematics and statistics; Computing methodologies > Symbolic and algebraic manipulation > Symbolic and algebraic algorithms; Information systems > Information retrieval > Document representation; Information systems > Information retrieval > Search engine architectures and scalability > Search engine indexing; Information systems > Information systems applications; Information systems > Information systems applications > Decision support systems > Data analytics; Mathematics of computing > Mathematical analysis > Numerical analysis > Interpolation; Theory of computation > Design and analysis of algorithms; subsequence matching; index interpolation; moving average transform; time-series databases
• ISBN: 9780769509662; 0769509665
• DOI: 10.5555/545538.545542

In this paper, we propose a subsequence matching algorithm that supports moving average transform of arbitrary order in time-series databases. The existing subsequence matching algorithm by Faloutsos et al. would require an index for each moving average order, which causes serious storage and CPU time overhead. In this paper, we solve the problem using index interpolation. The proposed algorithm can use only a few indexes for pre-selected moving average orders k and performs subsequence matching for arbitrary order m (≤ k). We prove that the proposed algorithm causes no false dismissal. For selectivities less than 10-2, the degradation of search performance compared with the fully-indexed case is no more than 17.2 % when two out of 128 indexes are used. The algorithm works better with smaller selectivities.