Abstract
In this paper we introduce a novel approach for fast retrieval of temporal patterns from time series data. This method constructs firstly an index over key subsequences, using subdimensional clustering. Then, during the querying process, rather than scanning the whole database, to extract relevant answers for a given query, our method suggests the traversal of the index represented as centroids of clusters, and search for similar subsequences.
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References
Gaber Mohamed et al. Mining data streams: a review. SIGMOD Rec., 34(2):18–26, 2005.
Ralanamahatana et al. Mining time series data. In DMKD’05, pages 1069–1103.
Lin et al. A symbolic representation of time series, with implications for streaming algorithms. In ACM SIGMOD RIDMKD Workshop 03, pages 2–11.
Wei et al. Efficient query filtering for streaming times series. ICDM ’05, pages 490–497.
Capitani et al. Warping the time on data streams. DKE, 62(3):438–458, September 2007.
Rafiei et al. Similarity-based queries for time series data. SIGMOD’97, pages 13–25.
Chakrabarti et al. Locally adaptive dimensionality reduction for indexing large time series databases. ACM TDS., 27(2):188–228, June 2002.
Lin Jessica et al. Rotation-invariant similarity in time series using bag-of-patterns representation. JIIS, 39(2):287–315, 2012.
Dhiral et al. Distance measures for effective clustering of arima time-series. In ICDM’01.
Guralnik et al. Event detection from time series data. KDD ’99, pages 33–42.
Pong et al. Efficient time series matching by wavelets. In ICDE99, pages 126–133.
Qiang et al. A multiresolution symbolic representation of ts. In ICDE05, pages 668–679.
Malinowsky Simon et al. 1d-sax; a novel symbolic representation for time series. In IDA’13.
Wang et al. Time series analysis with multiple resolutions. Inf. Syst., 35(1):56–74, 2010.
Aho et al. Efficient string matching: an aid to bibliographic search. com. ACM, 18(6), 1975.
Benabderrahmane et al. Intelligo: a new vector-based semantic similarity measure including annotation origin. BMC Bioinformatics, 11(1):588, 2010.
Salton et al. Introduction to Modern Information Retrieval. McGraw-Hill, NY, USA, 1983.
Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin.
Raymond et al. On the marriage of lp-norms and edit distance. In VLDB 04.
Vlachos et al. Indexing ts with support for multiple distance measures. KDD ’03.
Ucr database. http://www.cs.ucr.edu/eamonn/timeseriesdata.
Kelley et al. An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies. Protein eng., 9(11):1063–1065, 1996.
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Benabderrahmane, S. (2015). Temporal Constraints and Sub-Dimensional Clustering for Fast Similarity Search over Time Series Data. Application to Information Retrieval Tasks.. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_40
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DOI: https://doi.org/10.1007/978-3-319-08422-0_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08421-3
Online ISBN: 978-3-319-08422-0
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