Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

葡萄糖

On Compressing Historical Cliques in?Temporal Graphsd bioinformatics. Many real-world graphs change over time, with edges arriving continuously, and each edge has a timestamp representing the arrival time of that edge; such graphs are also known as temporal graphs. All maximal cliques in all snapshots since all possible historical moments are called

沒血色

體貼

MIPM: A Multidimensional Information Perception Model for?Estimating Time of?Arrival on?Real Road Nees. Thus, despite many existing works focusing on improving the efficiency and accuracy of the transportation system, however, few of them can handle multidimensional features on road networks. In this paper, we focus on a famous problem of the intelligent transportation system named estimated time

代理人

demote

TimeGAE: A Multivariate Time-Series Generation Method via?Graph Auto Encoders this issue, time series generation methods have emerged as a promising approach to alleviate data scarcity. However, most existing methods do not explicitly consider multivariate time series, thereby failing to fully exploit the potential spatial dependencies among different variables. The ability

過分自信

積極詞匯

Simulating Individual Infection Risk over?Big Trajectory Data attention. They are helpful in predicting epidemic transmission trends and mitigating the spread of infectious diseases. In this light, we study a new problem of Individual Infection Risk Assessment (IIRA) on the basis of fine-grained trajectory data. The problem aims to quantify the infection risk

aptitude

Flexible Contact Correlation Learning on?Spatio-Temporal Trajectoriesch or tracing, aiming to identify all trajectories in contact with a query trajectory. However, these studies only consider spatial contacts at specific timestamps, and highly rely on precise data with consistent sampling rates and aligned timestamps. In light of these limitations, we investigate th

depreciate

Inductive Spatial Temporal Prediction Under Data Drift with?Informative Graph Neural Networkms, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction accuracy by inducing data drift over time. Most existing studies extract invariant patterns to counter data drift but ignore pattern

內(nèi)行