Zobrazeno 1 - 10
of 173
pro vyhledávání: '"Katsifodimos, A"'
The advent of Large Language Models (LLMs) provides an opportunity to change the way queries are processed, moving beyond the constraints of conventional SQL-based database systems. However, using an LLM to answer a prediction query is still challeng
Externí odkaz:
http://arxiv.org/abs/2409.01140
Autor:
Liang, Jiaming, Lei, Chuan, Qin, Xiao, Zhang, Jiani, Katsifodimos, Asterios, Faloutsos, Christos, Rangwala, Huzefa
Data-centric AI focuses on understanding and utilizing high-quality, relevant data in training machine learning (ML) models, thereby increasing the likelihood of producing accurate and useful results. Automatic feature augmentation, aiming to augment
Externí odkaz:
http://arxiv.org/abs/2406.09534
Autor:
Siachamis, George, Psarakis, Kyriakos, Fragkoulis, Marios, van Deursen, Arie, Carbone, Paris, Katsifodimos, Asterios
Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors to handle failures while ensuring exactly-once processing guarantees. A
Externí odkaz:
http://arxiv.org/abs/2403.13629
Autor:
Koutras, Christos, Zhang, Jiani, Qin, Xiao, Lei, Chuan, Ioannidis, Vasileios, Faloutsos, Christos, Karypis, George, Katsifodimos, Asterios
How can we discover join relationships among columns of tabular data in a data repository? Can this be done effectively when metadata is missing? Traditional column matching works mainly rely on similarity measures based on exact value overlaps, henc
Externí odkaz:
http://arxiv.org/abs/2403.07653
Autor:
Psarakis, Kyriakos, Siachamis, George, Christodoulou, George, Fragkoulis, Marios, Katsifodimos, Asterios
Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However
Externí odkaz:
http://arxiv.org/abs/2312.06893
Autor:
Harte, Jesse, Zorgdrager, Wouter, Louridas, Panos, Katsifodimos, Asterios, Jannach, Dietmar, Fragkoulis, Marios
Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs), which are n
Externí odkaz:
http://arxiv.org/abs/2309.09261
The rapid growth of large-scale machine learning (ML) models has led numerous commercial companies to utilize ML models for generating predictive results to help business decision-making. As two primary components in traditional predictive pipelines,
Externí odkaz:
http://arxiv.org/abs/2306.08367
Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretabilit
Externí odkaz:
http://arxiv.org/abs/2207.09315
Autor:
Koutras, Christos, Hai, Rihan, Psarakis, Kyriakos, Fragkoulis, Marios, Katsifodimos, Asterios
How can we leverage existing column relationships within silos, to predict similar ones across silos? Can we do this efficiently and effectively? Existing matching approaches do not exploit prior knowledge, relying on prohibitively expensive similari
Externí odkaz:
http://arxiv.org/abs/2206.12733
Autor:
Hai, Rihan, Koutras, Christos, Ionescu, Andra, Li, Ziyu, Sun, Wenbo, van Schijndel, Jessie, Kang, Yan, Katsifodimos, Asterios
The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a major challenge: the integration and transformation of data demand a lot o
Externí odkaz:
http://arxiv.org/abs/2205.09681