An index-based approach for similarity search supporting time warping in large sequence databases

By Sang-Wook Kim, Sanghyun Park, Wesley W. Chu. Proceedings / International Conference on Data Engineering, 2001

340 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cc20fea56920a4310cfd59aad5679afd96a58205

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Eamonn J. Keogh
2. Raymond T. Ng
3. Tak-Chung Fu
4. Chotirat (Ann) Ratanamahatana
5. Philip Ka-Fai Chan
6. Stan Salvador
7. Lei Chen
8. Goce Trajcevski

In Signal Processing

1. Eamonn J. Keogh
2. Tak-Chung Fu
3. Rohit J. Kate
4. Luis Gravano
5. John Paparrizos
6. Raymond T. Ng
7. Chotirat (Ann) Ratanamahatana
8. Philip Ka-Fai Chan

Citations in the corpus

• On The Marriage of Lp-norms and Edit Distance (2004, methodology): “Specifically, even though DTW is not a metric, three lower bounds have been proposed [30, 18, 14].”
• A review on time series data mining (2011, methodology): “Kim et al. (2001) propose an indexing approach under DTW similarity measure.”
• Indexing multi-dimensional time-series with support for multiple distance measures (2003, methodology): “For DTW there have been a few approaches to provide a lower bound of the distance; we refer to them as LB-Kim [12] and LB-Yi [21].”
• Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping (2012, background): “This has led to a flurry of research on lower bounds, with at least eighteen proposed for DTW [1][6][20][21][33][40][41][42].”
• Toward accurate dynamic time warping in linear time and space (2007, neutral): “Indexing [9,14] uses lower-bounding functions to prune the number of times DTW is run for similarity search [17].”
• Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “Several lower bounding measures have been introduced to speed up similarity search using DTW [21, 26, 27, 44], and it has been shown that the amortized cost for computing DTW on large data sets is linear [21, 26].”
• Exact indexing of dynamic time warping (2002, methodology): “• Instead of testing on just one or two datasets, as is typical (Agrawal et al. 1995; Berndt and Clifford 1994; Chan et al. 2003; Faloutsos et al. 1994; Kim et al. 2001; Park et al. 1999, 2000, 2001), we tested all algorithms on 32 datasets.”
• Experimental comparison of representation methods and distance measures for time series data (2010, positive): “Several lower bounding measures have been introduced to speed up similarity search using DTW (Yi et al. 1998; Kim et al. 2001; Keogh 2002; Keogh and Ratanamahatana 2005), and it has been shown that the amortized cost for computing DTW on large data sets is linear (Keogh 2002; Keogh and Ratanamahatan”
• FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space (2004, background): “Indexing uses lower-bounding functions to prune out the number of times DTW needs to be run for certain tasks such as clustering a set of time series or finding the time series tha t is most similar to a given time series [6][10].”