The next-gen federated search architecture for biomedical knowledge repositories — The LIT-FED-SEARCH engine

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2024-06-13 DOI:10.1016/j.jocs.2024.102347

Filip Katulski , Maciej Malawski

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引用次数: 0

Abstract

The primary objective of LIT-FED-SEARCH software is to develop a user-friendly solution tailored to researchers and scientists. This solution aims to enhance their impact by facilitating the analysis of data from modern, extensive datasets like PubMed and Clinical Trials, alongside real-world evidence. The central concept we offer is a Federated Search Workflow Engine, which has been designed and maintained to accommodate various infrastructure configurations for the convenience of users. In line with this approach, potential users have the flexibility to configure their own computing environment and a set of interesting data repositories, based on their specific requirements and capabilities. This customization can significantly reduce the time and resources invested in research. LIT-FED-SEARCH is constructed with the support of OpenSearch full-text search engine as its heart. This paper offers an overview of the system’s architecture, capabilities, and potential applications in the field of biomedical research.

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生物医学知识库的下一代联合搜索架构--LIT-FED-SEARCH 引擎

LIT-FED-SEARCH 软件的主要目标是为研究人员和科学家量身定制用户友好型解决方案。该解决方案旨在通过促进对来自现代、广泛数据集（如 PubMed 和临床试验）的数据以及真实世界证据的分析，提高他们的影响力。我们提供的核心理念是联合搜索工作流引擎，该引擎的设计和维护能够适应各种基础设施配置，为用户提供方便。根据这种方法，潜在用户可以根据自己的具体要求和能力，灵活配置自己的计算环境和一组有趣的数据存储库。这种定制可以大大减少研究投入的时间和资源。LIT-FED-SEARCH 以 OpenSearch 全文搜索引擎为核心。本文概述了该系统的架构、功能以及在生物医学研究领域的潜在应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).