Abstract
Recent IoT-based applications generate time-series in a streaming fashion, and they often require techniques that enable environmental monitoring and event detection from generated time-series. Discovering a range motif, which is a subsequence that repetitively appears the most in a time-series, is a promising approach for satisfying such a requirement. This paper tackles the problem of monitoring a range motif of a streaming time-series under a count-based sliding-window setting. Whenever a window slides, a new subsequence is generated and the oldest subsequence is removed. A straightforward solution for monitoring a range motif is to scan all subsequences in the window while computing their occurring counts measured by a similarity function. Because the main bottleneck is similarity computation, this solution is not efficient. We therefore propose an efficient algorithm, namely SRMM. SRMM is simple and its time complexity basically depends only on the occurring counts of the removed and generated subsequences. Our experiments using four real datasets demonstrate that SRMM scales well and shows better performance than a baseline.
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References
Begum, N., Keogh, E.: Rare time series motif discovery from unbounded streams. PVLDB 8(2), 149–160 (2014)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Castro, N., Azevedo, P.: Multiresolution motif discovery in time series. In: SDM, pp. 665–676 (2010)
Chen, Y., Nascimento, M.A., Ooi, B.C., Tung, A.K.: SpADe: on shape-based pattern detection in streaming time series. In: ICDE, pp. 786–795 (2007)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD, pp. 493–498 (2003)
Grabocka, J., Schilling, N., Schmidt-Thieme, L.: Latent time-series motifs. TKDD 11(1), 6 (2016)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. KIS 3(3), 263–286 (2001)
Lam, H.T., Pham, N.D., Calders, T.: Online discovery of top-k similar motifs in time series data. In: SDM, pp. 1004–1015 (2011)
Li, Y., Zou, L., Zhang, H., Zhao, D.: Computing longest increasing subsequences over sequential data streams. PVLDB 10(3), 181–192 (2016)
Li, Y., Yiu, M.L., Gong, Z., et al.: Quick-motif: an efficient and scalable framework for exact motif discovery. In: ICDE, pp. 579–590 (2015)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15(2), 107–144 (2007)
Lucas, D., et al.: Designing optimal greenhouse gas observing networks that consider performance and cost. Geosci. Instrum. Methods Data Syst. 4(1), 121 (2015)
Moshtaghi, M., Leckie, C., Bezdek, J.C.: Online clustering of multivariate time-series. In: SDM, pp. 360–368 (2016)
Mueen, A., Keogh, E.: Online discovery and maintenance of time series motifs. In: KDD, pp. 1089–1098 (2010)
Mueen, A., Keogh, E., Zhu, Q., Cash, S., Westover, B.: Exact discovery of time series motifs. In: SDM, pp. 473–484 (2009)
Nguyen, H.L., Ng, W.K., Woon, Y.K.: Closed motifs for streaming time series classification. KIS 41(1), 101–125 (2014)
Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: ICDM, pp. 370–377 (2002)
Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format+ schema, pp. 1–14. Google Inc., White Paper (2011)
Shieh, J., Keogh, E.: i SAX: indexing and mining terabyte sized time series. In: KDD, pp. 623–631 (2008)
Yankov, D., Keogh, E., Medina, J., Chiu, B., Zordan, V.: Detecting time series motifs under uniform scaling. In: KDD, pp. 844–853 (2007)
Yeh, C.C.M., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: ICDM, pp. 1317–1322 (2016)
Zhu, Y., et al.: Matrix profile II: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins. In: ICDM, pp. 739–748 (2016)
Acknowledgement
This research is partially supported by JSPS Grant-in-Aid for Scientific Research (A) Grant Number JP26240013, JSPS Grant-in-Aid for Scientific Research (B) Grant Number JP17KT0082, and JSPS Grant-in-Aid for Young Scientists (B) Grant Number JP16K16056.
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Kato, S., Amagata, D., Nishio, S., Hara, T. (2018). Monitoring Range Motif on Streaming Time-Series. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_16
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DOI: https://doi.org/10.1007/978-3-319-98809-2_16
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