3 citations · 8 across the 6 of their papers we have counts for
7 papers
HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads
Xinyi Zhang, Liang Liang, Anastasia Ailamaki +1
Indexes are critical for efficient data retrieval and updates in modern databases. Recent advances in machine learning have led to the development of learned indexes, which model t…
The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…
Language-Integrated Recursive Queries (Full Version)
Anna Herlihy, Amir Shaikhha, Anastasia Ailamaki +1
Performance-critical industrial applications, including large-scale program, network, and distributed system analyses, rely on fixed-point computations. The introduction of recursi…
Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies
Kyoungmin Kim, Jiacheng Li, Kijae Hong +3
LLMs are increasingly used world-wide from daily tasks to agentic systems and data analytics, requiring significant GPU resources. While LLM inference systems are capable of servin…
Declarative Concurrent Data Structures
Aun Raza, Hamish Nicholson, Ioanna Tsakalidou +3
Implementing concurrent data structures is challenging and requires a deep understanding of concurrency concepts and careful design to ensure correctness, performance, and scalabil…
Efficient Data Access Paths for Mixed Vector-Relational Search
Viktor Sanca, Anastasia Ailamaki
The rapid growth of machine learning capabilities and the adoption of data processing methods using vector embeddings sparked a great interest in creating systems for vector data m…