Journal ArticleDOI
Trajectory similarity measures
Kevin Toohey,Matt Duckham +1 more
TLDR
This paper introduces and compares four of the most common measures of trajectory similarity: longest common subsequence (LCSS), Fréchet distance, dynamic time warping (DTW), and edit distance, implemented in a new open source R package.Abstract:
Storing, querying, and analyzing trajectories is becoming increasingly important, as the availability and volumes of trajectory data increases. One important class of trajectory analysis is computing trajectory similarity. This paper introduces and compares four of the most common measures of trajectory similarity: longest common subsequence (LCSS), Frechet distance, dynamic time warping (DTW), and edit distance. These four measures have been implemented in a new open source R package, freely available on CRAN [19]. The paper highlights some of the differences between these four similarity measures, using real trajectory data, in addition to indicating some of the important emerging applications for measurement of trajectory similarity.read more
Citations
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Journal ArticleDOI
Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented, and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.
Posted Content
Spatio-Temporal Data Mining: A Survey of Problems and Methods
TL;DR: A broad survey of this relatively young field of spatio-temporal data mining is presented and literature is classified into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining.
Journal ArticleDOI
A survey of trajectory distance measures and performance evaluation
TL;DR: A comprehensive survey of the trajectory distance measures is conducted, classified into four categories according to the trajectory data type and whether the temporal information is measured.
Proceedings ArticleDOI
DITA: Distributed In-Memory Trajectory Analytics
TL;DR: This work proposes an effective partitioning method, global index and local index, to address the data locality problem, and develops a filter-verification framework to improve the performance.
Proceedings ArticleDOI
Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces
TL;DR: It is argued that privacy-preserving synthesis of complete location traces can be an effective solution to this problem, and AdaTrace, a scalable location trace synthesizer with three novel features: provable statistical privacy, deterministic attack resilience, and strong utility preservation is presented.
References
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Proceedings Article
Using dynamic time warping to find patterns in time series
Donald J. Berndt,James Clifford +1 more
TL;DR: Preliminary experiments with a dynamic programming approach to pattern detection in databases, based on the dynamic time warping technique used in the speech recognition field, are described.
Journal ArticleDOI
Exact indexing of dynamic time warping
TL;DR: This work introduces a novel technique for the exact indexing of Dynamic time warping and proves its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
Proceedings ArticleDOI
Fast subsequence matching in time-series databases
TL;DR: An efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance.
Proceedings ArticleDOI
Discovering similar multidimensional trajectories
TL;DR: This work formalizes non-metric similarity functions based on the longest common subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to similar portions of the sequences.
Proceedings ArticleDOI
Robust and fast similarity search for moving object trajectories
TL;DR: Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences, indicate that EDR is more robust than Euclideans distance, DTW and ERP, and it is on average 50% more accurate than LCSS.