Robust and fast similarity search for moving object trajectories

By Lei Chen, M. Tamer Özsu, Vincent Oria. ACM SIGMOD Conference, 2005

1,423 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/efac1cdecf594038530fd4eebcec54bcfff4a36a

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

A review of moving object trajectory clustering algorithms (2016, methodology): “Dynamic time warping (DTW) method (Sankoff and Kruskal 1983; Chen et al. 2005) is a well-known technique to find an optimal alignment between two given (time-dependent) sequences under certain restrictions.”
Time-series clustering - A decade review (2015, methodology): “sequence (EDR) Elastic measure (one-to-many/one-to-none), uses a threshold pattern Shape-based [135]”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, methodology): “…[5] [26], distance based on Longest Common Subsequence (LCSS) [39],Edit Distance with Real Penalty (ERP) [8], Edit Distance on Real sequence (EDR) [9], DISSIM [14], Sequence Weighted Alignment model (Swale) [31], Spatial Assembling Distance (SpADe) [12] and similarity search based on Threshold…”
Discovery of convoys in trajectory databases (2008, methodology): “More r ecent proposals for trajectory distance functions include Longe st Common Subsequence (LCSS) [15], Edit Distance on Real Sequence (EDR) [10], and Edit distance with Real Penalty (ERP) [9].”
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases (2008, methodology): “…δ at the basis of our framework, is both used in the (k, δ)-anonymization process and in the querying process for evaluation purposes. io n In [4] it is introduced a set of six (Boolean) predicates that give a qualitative description of the relative position of a moving object τ with respect to…”
k-Shape: Efficient and Accurate Clustering of Time Series (2015, background): “Instead, starting with the application of DTW decades ago [8], research on that problem has focused on elastic distance measures that compare one-to-many or oneto-none points [11, 12, 44, 55, 78].”
Pattern graph tracking-based stock price prediction using big data (2018, methodology): “There are various methods for pattern matching such as Euclidean distance, Dynamic Time Warping (DTW)([4]), Edit Distance with Real Penalty (ERP)([15]), Longest Common Subsequence (LCSS)([44]) and Edit Distance on Real Sequence (EDR)([16]).”
A review on time series data mining (2011, background): “Chen et al. (2005a) further introduce a distance function based on an edit distance on real sequence (EDR), which is robust against the data imperfection.”
Discovering popular routes from trajectories (2011, background): “Other related work includes path planning by considering traffic uncertainty [17], searching similar trajectories [18], [19], [20], [21], [22], shortest path [6], [23], shortest path on time-dependent networks [24], finding the fastest path by speed patterns [25], etc.”
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, methodology): “These include a distance measure based on the longest common subsequence problem, Edit Distance with Real Penalty [29], Time Warp Edit distance [9] and Move-Split-Merge [4].”
Deep Representation Learning for Trajectory Similarity Computation (2018, methodology): “similarity has also been recognized by researchers, and a number of classic methods have been proposed, such as dynamic time wrapping (DTW) [6], longest common subsequence (LCSS) [7], edit distance with real penalty (ERP) [8], and edit distance on real sequences (EDR) [9].”
Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph (2017, methodology): “Approximate nearest neighbor search (ANNS) has been a hot topic over decades and provides fundamental support for many applications in data mining, databases, and information retrieval [2,10,12,23,37,42].”
The [Limited] Impact of Weather on Tourist Behavior in an Urban Destination (2015, methodology): “Many measures have been developed to identify the similarity of trajectories—distance-based measures, dynamic time wrapping, and longest common subsequence being some of the most frequently used (Chen, Özsu, and Oria 2005).”
Time series classification with ensembles of elastic distance measures (2015, methodology): “A second reason for using WDTW, LCSS, ERP, TWE, or MSM, rather than DTWCV, would be if they performed better on a particular problem domain.”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “Instead, starting with the application of DTW decades ago [7], research on that problem has focused on elastic distance measures that compare one-to-many or oneto-none points [9, 10, 37, 46, 64].”
Discovering spatio-temporal causal interactions in traffic data streams (2011, background): “However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected tra.c outliers has not been investigated before.”