Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation
systems. While there are several studies on the missing traffic data recovery in the last …

Background Carefully calibrated large-scale computational models of epidemic spread
represent a powerful tool to support the decision-making process during epidemic …

The missing traffic data has caused great obstacles and interference to further research,
such as traffic flow prediction, which affects the traffic authorities' judgment for the real traffic …

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports,
and climate science. Tensor latent factor models can describe higher-order correlations for …

Tensor decomposition operation is the basis for many data analysis tasks from clustering,
trend detection, anomaly detection, to correlation analysis. One key problem with tensor …

This paper presents a tensor-based rule-space management (TERM) system for improving
the available capacity of switches in software defined networking (SDN). Limited storage …

Existing tensor completion formulation mostly relies on partial observations from a single
tensor. However, tensors extracted from real-world data often are more complex due to:(i) …

Successfully tackling many urgent challenges in socio-economically critical domains, such
as public health and sustainability, requires a deeper understanding of causal relationships …

Data missing is very common in the spatial–temporal traffic data collected by various
detectors, and how to accurately impute the missing values is particularly important in …

Mobile edge computing in 5G network is emerging as a very promising computation
architecture by pushing computation and storage closer to end users with both strategically …