Knowledge construction from time series data using a collaborative exploration system

Guyet, Thomas; Garbay, Catherine; Dojat, Michel

doi:10.1016/j.jbi.2007.09.006

Cited by 42 publications

(24 citation statements)

References 17 publications

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“…Castro and Azevedo proposed a multiresolution motif discovery algorithm [4] that is both space and time efficient. Other approximate motifs algorithms exist [3,5,9,20,27,30]; however, one common drawback of all these algorithms is that they require an input parameter for the motif length.…”

Section: Related Workmentioning

confidence: 99%

Visualizing Variable-Length Time Series Motifs

Yuan¹,

Lin²,

Oates³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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The problem of time series motif discovery has received a lot of attention from researchers in the past decade. Most existing work on finding time series motifs require that the length of the motifs be known in advance. However, such information is not always available. In addition, motifs of different lengths may co-exist in a time series dataset. In this work, we develop a motif visualization system based on grammar induction. We demonstrate that grammar induction in time series can effectively identify repeated patterns without prior knowledge of their lengths. The motifs discovered by the visualization system are variable-lengths in two ways. Not only can the inter-motif subsequences have variable lengths, the intra-motif subsequences also are not restricted to have identical length-a unique property that is desirable, but has not been seen in the literature.

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Section: Related Workmentioning

confidence: 99%

Visualizing Variable-Length Time Series Motifs

Yuan¹,

Lin²,

Oates³

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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“…The advantage of probabilistic motif discovery algorithm is its efficiency. Other approximate motifs algorithms exist [10,12,32,42,45,52]; however, one common drawback for all these algorithms is that they require an input parameter for the motif length.…”

Section: Time Series Motifsmentioning

confidence: 99%

Approximate variable-length time series motif discovery using grammar inference

Lin

2010

Proceedings of the Tenth International Workshop on Multimedia Data Mining

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The problem of identifying frequently occurring patterns, or motifs, in time series data has received a lot of attention in the past few years. Most existing work on finding time series motifs require that the length of the patterns be known in advance. However, such information is not always available. In addition, motifs of different lengths may co-exist in a time series dataset. In this work, we propose a novel approach, based on grammar induction, for approximate variable-length time series motif discovery. Our algorithm offers the advantage of discovering hierarchical structure, regularity and grammar from the data. The preliminary results are promising. They show that the grammar-based approach is able to find some important motifs, and suggest that the new direction of using grammar-based algorithms for time series pattern discovery might be worth exploring.

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“…Given this, most of the literature has focused on fast approximate algorithms for motif discovery (Beaudoin et al 2008;Chiu et al 2003;Minnen et al 2007b;Tanaka et al 2005;Guyet et al 2007;Meng et al 2008;Rombo and Terracina 2004;Lin et al 2002). For example, a recent paper on finding approximate motifs reports taking 343 s to find motifs in a dataset of length 32,260 (Meng et al 2008), in contrast we can find motifs in similar datasets exactly, and on similar hardware in under 100 s. Similarly, another very recent paper reports taking 15 min to find approximate motifs in a dataset of size 111,848 (Beaudoin et al 2008), however we can find motifs in similar datasets in under 4 min.…”

Section: Related Workmentioning

confidence: 99%

A disk-aware algorithm for time series motif discovery

Mueen

Keogh

Zhu

et al. 2010

Data Min Knowl Disc

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Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

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Knowledge construction from time series data using a collaborative exploration system

Cited by 42 publications

References 17 publications

Visualizing Variable-Length Time Series Motifs

Visualizing Variable-Length Time Series Motifs

Approximate variable-length time series motif discovery using grammar inference

A disk-aware algorithm for time series motif discovery

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