Towards a multi-agents model for errors detection and correction in big data flows

Snineh, Sidi Mohamed; Bouattane, Omar; Youssfi, Mohamed; Daaif, Abdelaziz

doi:10.1109/icds47004.2019.8942297

Cited by 1 publication

(2 citation statements)

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Section: Visual and Geometric Datamentioning

confidence: 99%

“…Incorrect external keys for representing the related columns in two tables and linking the information from two tables [150]; redundant information items having inconsistent values at different parts of the database [151,152]; missing relationships between two tables while the link should exist for linking common columns in two tables [153,154] Sensory Data Errors or missing values in time series sensory data that measures structure vibrations [155] Metadata Errors in the metadata for specifying the formats and organization of datasets, such as the meaning of columns of numbers in a data file [144]; errors in the metadata for specifying the time and data collection environments [156]; errors in the metadata for specifying the methods of processing and transforming the data, such as a transformation matrix for transforming point clouds to a global coordinate system [157] Model 2D/3D Maps Location errors of points [158]; length and direction errors of lines representing paths on 2D or 3D maps [159]; level of detail of maps [160]; missing values in the properties of objects on 2D or 3D maps [161] Semantic-Rich Digital Models Missing and additional objects [162]; dimensional and shape deviations from actual dimensions [163,164]; wrong type information of objects [165] These data-quality studies still have not yet addressed some challenges related to quantifying the data and model reliability in engineering application contexts. Compared with systematic quality quantification of structured data and images, one challenge is that relatively limited studies examined metrics for measuring the quality of audio, video, and natural language reports (Taleb et al 2018 [166]).…”

Section: Datamentioning

confidence: 99%

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A Critical Review for Trustworthy and Explainable Structural Health Monitoring and Risk Prognosis of Bridges with Human-In-The-Loop

et al. 2023

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Trustworthy and explainable structural health monitoring (SHM) of bridges is crucial for ensuring the safe maintenance and operation of deficient structures. Unfortunately, existing SHM methods pose various challenges that interweave cognitive, technical, and decision-making processes. Recent development of emerging sensing devices and technologies enables intelligent acquisition and processing of massive spatiotemporal data. However, such processes always involve human-in-the-loop (HITL), which introduces redundancies and errors that lead to unreliable SHM and service safety diagnosis of bridges. Comprehending human-cyber (HC) reliability issues during SHM processes is necessary for ensuring the reliable SHM of bridges. This study aims at synthesizing studies related to HC reliability for supporting the trustworthy and explainable SHM of bridges. The authors use a bridge inspection case to lead a synthesis of studies that examined techniques relevant to the identified HC reliability issues. This synthesis revealed challenges that impede the industry from monitoring, predicting, and controlling HC reliability in bridges. In conclusion, a research road map was provided for addressing the identified challenges.

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