Computing trajectory similarities is a critical and fundamental task for various spatial-
temporal applications, such as clustering, prediction, and anomaly detection. Traditional …

The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Trajectory similarity computation is a fundamental problem for various applications in
trajectory data analysis. However, the high computation cost of existing trajectory similarity …

Advances of the sensor and GPS techniques have motivated the proliferation of trajectory
data in a wide spectrum of applications. Trajectory similarity computation is one of the most …

Trajectory similarity computation is a core problem in the field of trajectory data queries.
However, the high time complexity of calculating the trajectory similarity has always been a …

Brain electroencephalography (EEG), the complex, weak, multivariate, nonlinear, and
nonstationary time series, has been recently widely applied in neurocognitive disorder …

Trajectory similarity computation is the cornerstone of many applications in the field of
trajectory data analysis. To cope with the high time complexity of calculating exact similarity …

We study several polygonal curve problems under the Fréchet distance via algebraic
geometric methods. Let 𝕏 dm and 𝕏 dk be the spaces of all polygonal curves of m and k …

Time series similarity computation is a fundamental primitive that underpins many time
series data analysis tasks. However, many existing time series similarity measures have a …

We study the c-approximate near neighbor problem under the continuous Fréchet distance:
Given a set of n polygonal curves with m vertices, a radius δ> 0, and a parameter k≤ m, we …