Repairing raw metadata for metadata management

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-01-20 DOI:10.1016/j.is.2024.102344

Hiba Khalid, Esteban Zimányi

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引用次数: 0

Abstract

With the exponential growth of data production, the generation of metadata has become an integral part of the process. Metadata plays a crucial role in facilitating enhanced data analytics, data integration, and resource management by offering valuable insights. However, inconsistencies arise due to deviations from standards in metadata recording, including missing attribute information, publishing URLs, and provenance. Furthermore, the recorded metadata may exhibit inconsistencies, such as varied value formats, special characters, and inaccurately entered values. Addressing these inconsistencies through metadata preparation can greatly enhance the user experience during data management tasks.

This paper introduces MDPrep, a system that explores the usability and applicability of data preparation techniques in improving metadata quality. Our approach involves three steps: (1) detecting and identifying problematic metadata elements and structural issues, (2) employing a keyword-based approach to enhance metadata elements and a syntax-based approach to rectify structural metadata issues, and (3) comparing the outcomes to ensure improved readability and reusability of prepared metadata files.

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为元数据管理修复原始元数据

随着数据生产的指数级增长，元数据的生成已成为数据生产过程中不可或缺的一部分。元数据通过提供有价值的见解，在促进增强数据分析、数据整合和资源管理方面发挥着至关重要的作用。然而，由于元数据记录偏离了标准，包括缺少属性信息、发布 URL 和出处，因此会出现不一致的情况。此外，记录的元数据也可能表现出不一致，如不同的值格式、特殊字符和不准确的输入值。本文介绍的 MDPrep 是一个探索数据准备技术在提高元数据质量方面的可用性和适用性的系统。我们的方法包括三个步骤：我们的方法包括三个步骤：(1) 检测和识别有问题的元数据元素和结构问题；(2) 采用基于关键字的方法来增强元数据元素，并采用基于语法的方法来纠正元数据结构问题；(3) 比较结果，以确保提高准备好的元数据文件的可读性和可重用性。

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

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.