In the landscape of spatio-temporal data analytics, effective trajectory
representation learning is paramount. To bridge the gap of learning accurate
representations with efficient and flexible mechanisms, we introduce Efflex, a
comprehensive pipeline for transformative graph modeling and representation
learning of the large-volume spatio-temporal trajectories. Efflex pioneers the
incorporation of a multi-scale k-nearest neighbors (KNN) algorithm with feature
fusion for graph construction, marking a leap in dimensionality reduction
techniques by preserving essential data features. Moreover, the groundbreaking
graph construction mechanism and the high-performance lightweight GCN increase
embedding extraction speed by up to 36 times faster. We further offer Efflex in
two versions, Efflex-L for scenarios demanding high accuracy, and Efflex-B for
environments requiring swift data processing. Comprehensive experimentation
with the Porto and Geolife datasets validates our approach, positioning Efflex
as the state-of-the-art in the domain. Such enhancements in speed and accuracy
highlight the versatility of Efflex, underscoring its wide-ranging potential
for deployment in time-sensitive and computationally constrained applications.

在时空数据分析的领域中，Efflex 是一种综合性管道，用于对大容量时空轨迹进行图模型建模和表示学习，通过多尺度 k 最近邻算法和特征融合实现了维度约简，并提高了嵌入提取的速度，为高精度和快速数据处理提供了可能。

Efflex: 高效灵活的空时轨迹图建模与表示学习的管线

Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory  Graph Modeling and Representation Learning

Emergency relief operations are essential in disaster aftermaths,
necessitating effective resource allocation to minimize negative impacts and
maximize benefits. In prolonged crises or extensive disasters, a systematic,
multi-cycle approach is key for timely and informed decision-making. Leveraging
advancements in IoT and spatio-temporal data analytics, we've developed the
Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM). This
multi-constraint, multi-objective resource allocation model has been rigorously
tested against 28 diverse challenges, showing superior performance in
comparison to established models such as NSGA-II, IBEA, and MOEA/D. MSGW-FLM's
effectiveness is particularly notable in complex, multi-cycle emergency rescue
scenarios, which involve numerous constraints and objectives. This model
represents a significant step forward in optimizing resource distribution in
emergency response situations.

利用物联网和时空数据分析，我们开发了多目标洗牌灰狼青蛙跳模型（MSGW-FLM），该模型在应对复杂的多周期紧急救援场景中显示出了卓越的性能，为优化应急救援资源分配迈出了重要的一步。