Zobrazeno 1 - 10
of 310
pro vyhledávání: '"AILAMAKI, ANASTASIA"'
Autor:
Kim, Kyoungmin, Ailamaki, Anastasia
In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM output
Externí odkaz:
http://arxiv.org/abs/2412.18022
The growing usage of Large Language Models (LLMs) highlights the demands and challenges in scalable LLM inference systems, affecting deployment and development processes. On the deployment side, there is a lack of comprehensive analysis on the condit
Externí odkaz:
http://arxiv.org/abs/2411.07447
Serverless query processing has become increasingly popular due to its auto-scaling, high elasticity, and pay-as-you-go pricing. It allows cloud data warehouse (or lakehouse) users to focus on data analysis without the burden of managing systems and
Externí odkaz:
http://arxiv.org/abs/2409.01388
Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces
Externí odkaz:
http://arxiv.org/abs/2405.19784
Autor:
Raza, Aun, Nicholson, Hamish, Tsakalidou, Ioanna, Herlihy, Anna, Tagore, Prathamesh, Ailamaki, Anastasia
Implementing concurrent data structures is challenging and requires a deep understanding of concurrency concepts and careful design to ensure correctness, performance, and scalability. Further, composing operations on two or more concurrent data stru
Externí odkaz:
http://arxiv.org/abs/2404.13359
Autor:
Sanca, Viktor, Ailamaki, Anastasia
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 management. While the predominant approach of vector data management is
Externí odkaz:
http://arxiv.org/abs/2403.15807
Performance-critical industrial applications, including large-scale program, network, and distributed system analyses, are increasingly reliant on recursive queries for data analysis. Yet traditional relational algebra-based query optimization techni
Externí odkaz:
http://arxiv.org/abs/2312.04282
Collecting data, extracting value, and combining insights from relational and context-rich multi-modal sources in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators allow declarative and optimiz
Externí odkaz:
http://arxiv.org/abs/2312.01476
Analytical tools often require real-time responses for highly concurrent parameterized workloads. A common solution is to answer queries using materialized subexpressions, hence reducing processing at runtime. However, as queries are still processed
Externí odkaz:
http://arxiv.org/abs/2307.08018
Autor:
Psaroudakis, Iraklis, Kissinger, Thomas, Porobic, Danica, Ilsche, Thomas, Liarou, Erietta, Tözün, Pınar, Ailamaki, Anastasia, Lehner, Wolfgang
Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goa
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A79860
https://tud.qucosa.de/api/qucosa%3A79860/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A79860/attachment/ATT-0/