Zobrazeno 1 - 10
of 305
pro vyhledávání: '"Ailamaki, Anastasia"'
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 hardware and software management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing gre
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/
Autor:
Sanca, Viktor, Ailamaki, Anastasia
As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such for
Externí odkaz:
http://arxiv.org/abs/2212.07517
Autor:
Mancini, Riccardo, Karthik, Srinivas, Chandra, Bikash, Mageirakos, Vasilis, Ailamaki, Anastasia
Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables
Externí odkaz:
http://arxiv.org/abs/2202.13511