提高物联网大数据在决策中的准确性

Xiaoli Liu, S. Tamminen, Xiang Su, Pekka Siirtola, J. Röning, J. Riekki, Jussi Kiljander, J. Soininen
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引用次数: 10

摘要

数据对支持决策至关重要。如果数据的准确性很低,决策就不太可能是合理的。物联网(IoT)产生的大数据不准确、不一致、不完整、欺骗和模型近似。提高数据准确性对于应对这些挑战非常重要。在本文中,我们总结了影响数据处理和决策的物联网的关键特征和挑战。我们回顾了测量和提高数据准确性以及挖掘不确定数据流的前景。此外,我们还就物联网数据的异构和分布式特性、自主决策、上下文感知和领域优化方法、物联网边缘设备的数据清洗和处理技术以及隐私保护、个性化和安全数据管理等方面的未来发展提出了五项建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Veracity of IoT Generated Big Data in Decision Making
Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.
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