Data quality and the Internet of Things

Liu, Caihua; Nitschke, Patrick; Williams, Susan P.; Zowghi, Didar

doi:10.1007/s00607-019-00746-z

Cited by 42 publications

(24 citation statements)

References 24 publications

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“…This review builds most initially on other surveys [ 7 , 67 , 68 , 69 ] and reviews [ 5 , 10 , 70 ]. Two of the most important surveys, including (1) Batini et al [ 6 ], summarize 13 methodologies for DQ assessment and improvement up to 2009, and (2) Cichy et al [ 24 ] summarize 12 methodologies/frameworks applicable to information systems or business data published up to 2020, and 13 special-purpose DQ frameworks (including two ISO standards).…”

Section: Data Quality Management Techniques Reviewmentioning

confidence: 91%

“…The survey by Qin et al [ 9 ] investigated some key research areas in the IoT from a data-centric perspective and did not specifically focus on DQ assessment techniques. Liu et al [ 10 ] reviewed and analyzed 45 studies of DQ in the IoT from 1999 to 2018, focused on the dimensions of DQ and approaches to measure them. The IoT is the source of a lot of Big Data (device data), and the analysis of a lot of device data depends on Big Data.…”

Section: Introductionmentioning

confidence: 99%

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Data Quality Management in the Internet of Things

Zhang

Jeong

Lee

2021

Sensors

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Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique.

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Section: Data Quality Management Techniques Reviewmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Data Quality Management in the Internet of Things

Zhang

Jeong

Lee

2021

Sensors

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“…The consistency , according to ISO [ 12 ], refers to “The degree to which data has attributes that are free from contradiction and are coherent with other data in a specific context of use”. It is also referred to as concordance in some works [ 13 ]. In our context, the consistency dimension corresponds to the degree of coherence between IoT data events sent by different IoT data sources and related to the same shipment .…”

Section: Related Workmentioning

confidence: 99%

“…The IoT data quality issue has been addressed using data quality dimensions. For this purpose, the traditional data quality dimensions [ 11 ] have been used and adapted to the IoT context needs [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…The currentness was defined by ISO [12] as: "The degree to which data has attributes that are of the right age in a specific context of use". It is also referred to as timeliness, currency, freshness, delay or contemporaneous, in some works [13,14]. In our context, an event is considered of the right age when it is received at the expected time according to the update interval agreed by the stakeholders and defined in the smart contract.…”

Section: Data Quality Definitionsmentioning

confidence: 99%

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IoT Data Qualification for a Logistic Chain Traceability Smart Contract

Ahmed

Taconet

Ould³

et al. 2021

Sensors

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In the logistic chain domain, the traceability of shipments in their entire delivery process from the shipper to the consignee involves many stakeholders. From the traceability data, contractual decisions may be taken such as incident detection, validation of the delivery or billing. The stakeholders require transparency in the whole process. The combination of the Internet of Things (IoT) and the blockchain paradigms helps in the development of automated and trusted systems. In this context, ensuring the quality of the IoT data is an absolute requirement for the adoption of those technologies. In this article, we propose an approach to assess the data quality (DQ) of IoT data sources using a logistic traceability smart contract developed on top of a blockchain. We select the quality dimensions relevant to our context, namely accuracy, completeness, consistency and currentness, with a proposition of their corresponding measurement methods. We also propose a data quality model specific to the logistic chain domain and a distributed traceability architecture. The evaluation of the proposal shows the capacity of the proposed method to assess the IoT data quality and ensure the user agreement on the data qualification rules. The proposed solution opens new opportunities in the development of automated logistic traceability systems.

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A Proactive Decision-Driven Architecture for Information-Aware Internet-of-Things Applications

Darwish

2021

Advances in Intelligent Systems and Computing

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Data quality and the Internet of Things

Cited by 42 publications

References 24 publications

Data Quality Management in the Internet of Things

Data Quality Management in the Internet of Things

IoT Data Qualification for a Logistic Chain Traceability Smart Contract

A Proactive Decision-Driven Architecture for Information-Aware Internet-of-Things Applications

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