23 citations · 63 across the 23 of their papers we have counts for
11 papers · 1 filter
From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
Lingze Zeng, Shaofeng Cai, Changshuo Liu +3
Relational data stored in RDBMS is foundational to many real-world applications across domains such as e-commerce, finance, and sociality. While deep neural networks (DNNs) have ac…
Towards Effective Orchestration of AI x DB Workloads
Naili Xing, Haotian Gao, Zhanhao Zhao +6
AI-driven analytics are increasingly crucial to data-centric decision-making. Executing relational and AI operators in separate runtimes prevents the database optimizer and runtime…
NeurIDA: Dynamic Modeling for Effective In-Database Analytics
Lingze Zeng, Naili Xing, Shaofeng Cai +4
Relational Database Management Systems (RDBMS) manage complex, interrelated data and support a broad spectrum of analytical tasks. With the growing demand for predictive analytics,…
NeurStore: Efficient In-database Deep Learning Model Management System
Siqi Xiang, Sheng Wang, Xiaokui Xiao +3
With the prevalence of in-database AI-powered analytics, there is an increasing demand for database systems to efficiently manage the ever-expanding number and size of deep learnin…
In-Context Adaptation to Concept Drift for Learned Database Operations
Jiaqi Zhu, Shaofeng Cai, Yanyan Shen +3
Machine learning has demonstrated transformative potential for database operations, such as query optimization and in-database data analytics. However, dynamic database environment…
HAKES: Scalable Vector Database for Embedding Search Service
Guoyu Hu, Shaofeng Cai, Tien Tuan Anh Dinh +4
Modern deep learning models capture the semantics of complex data by transforming them into high-dimensional embedding vectors. Emerging applications, such as retrieval-augmented g…
NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling
Zhanhao Zhao, Haotian Gao, Naili Xing +5
Learned database components, which deeply integrate machine learning into their design, have been extensively studied in recent years. Given the dynamism of databases, where data a…
Modeling Concurrency Control as a Learnable Function
Hexiang Pan, Shaofeng Cai, Tien Tuan Anh Dinh +4
Concurrency control (CC) algorithms are important in modern transactional databases, as they enable high performance by executing transactions concurrently while ensuring correctne…
NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
Zhanhao Zhao, Shaofeng Cai, Haotian Gao +8
Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry…
NeurDB: An AI-powered Autonomous Data System
Beng Chin Ooi, Shaofeng Cai, Gang Chen +9
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises…
Powering In-Database Dynamic Model Slicing for Structured Data Analytics
Lingze Zeng, Naili Xing, Shaofeng Cai +4
Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract s…