5 papers
Updatable Balanced Index for Fast On-device Search with Auto-selection Model
Yushuai Ji, Sheng Wang, Zhiyu Chen +2
Diverse types of edge data, such as 2D geo-locations and 3D point clouds, are collected by sensors like lidar and GPS receivers on edge devices. On-device searches, such as k-neare…
A Unified Approach for Multi-Granularity Search over Spatial Datasets
Wenzhe Yang, Sheng Wang, Shixun Huang +4
There has been increased interest in data search as a means to find relevant datasets or data points in data lakes and repositories. Although approaches have been proposed to suppo…
FedAPM: Federated Learning via ADMM with Partial Model Personalization
Shengkun Zhu, Feiteng Nie, Jinshan Zeng +6
In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, an…
Joinable Search over Multi-source Spatial Datasets: Overlap, Coverage, and Efficiency
Wenzhe Yang, Sheng Wang, Zhiyu Chen +2
The search for joinable data is pivotal for numerous applications, such as data integration, data augmentation, and data analysis. Although there have been many successful joinable…
On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means
Yushuai Ji, Zepeng Liu, Sheng Wang +2
The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large…