A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities

IF 7.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Big Data Pub Date : 2024-07-05 DOI:10.1109/TBDATA.2024.3423677

Shuang Wang;He Zhang;Quan Z. Sheng;Xiaoping Li;Zhu Sun;Taotao Cai;Wei Emma Zhang;Jian Yang;Qing Gao

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引用次数: 0

Abstract

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

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真理发现综述：概念、方法、应用和机遇

在数据信息爆炸的时代，在网络、社会传感、人群传感、数据传感等应用中，对一个物体（如喜马拉雅山的高度）有不同来源的观测结果。来自不同来源的对一个对象的观察可能会由于错误、缺失记录、打字错误、过时数据等而相互冲突。如何从各种来源中发现事物的真相是必要和紧迫的。在本文中，我们的目标是从概念、方法、应用和机会的角度对真理发现问题进行全面而详尽的调查。我们首先从对象、来源和观察中系统地审查和比较问题。基于这些问题性质，从单值或多值观测、独立源或依赖源、静态源或动态源、监督学习或无监督学习等方面对不同方法进行了深入的分析和比较，并对不同场景下的应用进行了调查。为了今后在真值发现领域的研究，我们总结了上述方法使用的代码源和数据集。最后，我们指出了真相发现的潜在挑战和机遇，以期对这一领域的进一步研究有所启示和推动。

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

IEEE Transactions on Big Data Multiple-

CiteScore

11.80

自引率

2.80%

发文量

114

期刊介绍： The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.