Scholarlink

Abstract <p>Modern high-energy physics (HEP) experiments generate and store vast volumes of data, which users access through complex and irregular patterns. Efficient data management in such environments requires accurate forecasting of dataset popularity to optimize storage, caching, and data distribution strategies. In this work, we propose an approach for predicting future dataset access patterns using transformer-based deep learning models. By leveraging historical logs of user interactions with HEP datasets, our method captures temporal dependences and contextual signals to forecast both short- and medium-term data demand.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Anticipating Data Demand in HEP: A Transformer Approach

  • 期刊论文

  • 发表日期：

    2026年7月20日

查看全文
  • M. A. Grigorieva,
  • N. N. Popova,
  • M. V. Shubin

摘要

Abstract

Modern high-energy physics (HEP) experiments generate and store vast volumes of data, which users access through complex and irregular patterns. Efficient data management in such environments requires accurate forecasting of dataset popularity to optimize storage, caching, and data distribution strategies. In this work, we propose an approach for predicting future dataset access patterns using transformer-based deep learning models. By leveraging historical logs of user interactions with HEP datasets, our method captures temporal dependences and contextual signals to forecast both short- and medium-term data demand.