Indexing spatio-temporal trajectories with Chebyshev polynomials

By Yuhan Cai, Raymond T. Ng. ACM SIGMOD Conference, 2004

361 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d108d181417d8e18d3dde91d1f645179e95130ce

How researchers see this paper

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

In Signal Processing

Citations in the corpus

k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “The ubiquity of time series has generated a substantial interest in querying [2, 38, 39, 41, 52, 61, 65], indexing [8, 11, 34, 35, 37, 63], classification [30, 47, 58, 70], clustering [36, 45, 54, 69, 71], and modeling [3, 31, 68] of such data.”
k-Shape: Efficient and Accurate Clustering of Time Series (2015, neutral): “est in querying [2, 19, 45, 46, 48, 62, 74, 79], indexing [9, 13, 41, 42, 44, 77], classification [37, 56, 68, 88], clustering [43, 54, 64, 87, 89], and modeling [4, 38, 86] of such data.”
iSAX: indexing and mining terabyte sized time series (2008, methodology): “The list of such representations includes (in approximate order of introduction) the discrete Fourier transform (DFT) [6], the discrete Cosine transform (DCT), the discrete Wavelet transform (DWT), Piecewise Aggregate Approximation (PAA) [8], Adaptive Piecewise Constant Approximation (APCA), Chebysh”
Experimental comparison of representation methods and distance measures for time series data (2010, methodology): “Many techniques have been proposed for representing time series with reduced dimensionality, for example: Discrete Fourier Transformation (DFT) (Faloutsos et al. 1994), Single Value Decomposition (SVD) (Faloutsos et al. 1994), Discrete Cosine Transformation (DCT) (Korn et al. 1997), Discrete Wavelet”
Time-series clustering - A decade review (2015, methodology): “The methods in this group are Wavelets [85]: HAAR, DAUBECHIES, Coeiflets, Symlets, Discrete Wavelet Transform(DWT), spectral Chebyshev Polynomials [99], spectral DFT [20], Random Mappings [100], Piecewise Aggregate Approximation (PAA) [24] and Indexable Piecewise Linear Approximation (IPLA) [101].”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “In contrast, in Figure 5 we can see that highly periodic data can slightly favor the spectral representations (DCT, DFT, CHEB) over the polynomial representations (SAX, APCA, DWT/PAA, IPLA).”
Robust and fast similarity search for moving object trajectories (2005, methodology): “In this experiment, we use ASL data set from UCI KDD archive3, Kungfu and Slip data sets from [ 5 ].”
Time-series data mining (2012, methodology): “Finally, an approximation by Chebychev polynomials [Cai and Ng 2004] has also been proposed but the results obtained have later been withdrawn due to an error in implementation.”