STORM: Exploiting Spatiotemporal Continuity for Trajectory Similarity Learning in Road Networks [0.03%]
利用时空连续性的道路网络中基于轨迹的相似性学习(STORM)
Jialiang Li,Hua Lu,Cyrus Shahabi
Jialiang Li
Trajectory similarity in road networks is pivotal for numerous applications in transportation, urban planning, and ridesharing. However, due to the varying lengths of trajectories, employing similarity metrics directly on raw trajectory dat...
Zhipeng Luo,Qiang Gao,Yazhou He et al.
Zhipeng Luo et al.
Learning classification models from real-world data often requires substantial human effort devoted to instance annotation. As the instance-based annotating process can be very time-consuming and costly, we propose a novel active learning f...
Data Synthesis Reinvented: Preserving Missing Patterns for Enhanced Analysis [0.03%]
重构数据合成：保留缺失模式以增强分析能力
Xinyue Wang,Hafiz Asif,Shashank Gupta et al.
Xinyue Wang et al.
Synthetic data is being widely used as a replacement or enhancement for real data in fields as diverse as healthcare, telecommunications, and finance. Unlike real data, which represents actual people and objects, synthetic data is generated...
Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity [0.03%]
CAFÉ：利用缺失数据的异构性改进联合数据插补
Sitao Min,Hafiz Asif,Xinyue Wang et al.
Sitao Min et al.
Federated learning (FL), a decentralized machine learning approach, offers great performance while alleviating autonomy and confidentiality concerns. Despite FL's popularity, how to deal with missing values in a federated manner is not well...
A Neural Database for Answering Aggregate Queries on Incomplete Relational Data [0.03%]
处理不完整数据的聚合查询的神经数据库系统
Sepanta Zeighami,Raghav Seshadri,Cyrus Shahabi
Sepanta Zeighami
Real-world datasets are often incomplete due to data collection cost, privacy considerations or as a side effect of data integration/preparation. We focus on answering aggregate queries on such datasets, where data incompleteness causes the...
Weakly Supervised Concept Map Generation through Task-Guided Graph Translation [0.03%]
通过任务引导的图转换实现弱监督概念图生成
Jiaying Lu,Xiangjue Dong,Carl Yang
Jiaying Lu
Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization of knowledge from free texts. Traditional unsupervised methods do not generate task-or...
Yun William Yu,Griffin M Weber
Yun William Yu
In this extended abstract, we describe and analyze a lossy compression of MinHash from buckets of size O(logn) to buckets of size O(loglogn) by encoding using floating-point notation. This new compressed sketch, which we call HyperMinHash, ...
Hafiz Asif,Jaideep Vaidya,Periklis A Papakonstantinou
Hafiz Asif
Identifying anomalies in data is vital in many domains, including medicine, finance, and national security. However, privacy concerns pose a significant roadblock to carrying out such an analysis. Since existing privacy definitions do not a...
Matteo Paganelli,Paolo Sottovia,Kwanghyun Park et al.
Matteo Paganelli et al.
In the past decade, many approaches have been suggested to execute ML workloads on a DBMS. However, most of them have looked at in-DBMS ML from a training perspective, whereas ML inference has been largely overlooked. We think that this is ...
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark [0.03%]
异构网络表示学习：统框架、调查与基准测试
Carl Yang,Yuxin Xiao,Yu Zhang et al.
Carl Yang et al.
Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhil...