Abstract
Time series motif discovery is an important primitive for the time series data mining. With the explosion of new sensing technology, there is a continuously increasing amount of time series data in every aspect of our lives, from seismology, entomology, human activity monitoring, medicine and so on. Considering the rich information included in time series, motif discovery has become an essential part of many data mining tasks. In recent years, the problem of consensus motif discovery in multiple time series begins to appear in our vision. For this task, the existing approaches can only search the consensus motif of a fixed length. However, variable-length motif mining is more common in real applications. To address this problem, the brute force version of the existing fixed-length approach is prohibitively expensive. In this paper, we propose an efficient, scalable and exact algorithm VACOMI to search the consensus motif of all lengths in a given motif length range. We evaluate the performance of VACOMI on four real datasets. The results show that VACOMI can reduce up to 96% of the running time compared with the state-of-the-art approach.
The work is supported by the Ministry of Science and Technology of China, National Key Research and Development Program (No. 2020YFB1710001).
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Zhang, M., Wang, P., Wang, W. (2022). Efficient Consensus Motif Discovery of All Lengths in Multiple Time Series. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_39
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DOI: https://doi.org/10.1007/978-3-031-00126-0_39
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