Potential Problem Data Tagging: Augmenting information systems with the capability to deal with inaccuracies

Woodall, Philip; Giannikas, Vaggelis; Lu, Wenrong; McFarlane, Duncan

doi:10.1016/j.dss.2019.04.007

Cited by 6 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In business, data quality increases firms' ability to find useful information from data and improves firms' "decision quality" [38]. In light of these urgent problems, several authors have proposed automated methods and processes to discover data quality issues [39], [40]. One method relied on the discovery of poor data that may lead to errors in tests and, as a result, may lead to the inability to reproduce results [41].…”

Section: B Data Quality Issues and Methodsmentioning

confidence: 99%

Incorrect data in the widely used Inside Airbnb dataset

Alsudais

2021

Decision Support Systems

View full text Add to dashboard Cite

Section: B Data Quality Issues and Methodsmentioning

confidence: 99%

Incorrect data in the widely used Inside Airbnb dataset

Alsudais

2021

Decision Support Systems

View full text Add to dashboard Cite

“…The tagging of data causes potential problems, and ISO 8000-61 was proposed as a method to address the inaccuracy of the information system. These methods involve fundamental data processes such as noise detection or sensor activation, and missing data or diversity of data representation [14].…”

Section: Data Qualitymentioning

confidence: 99%

Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory

Park

Cho

et al. 2022

Sustainability

View full text Add to dashboard Cite

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In previous studies, a machine, conducting a single continuous operation, has been mainly observed for power estimation. However, a modularized production line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of such production lines, it is important to interpret and distinguish mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline from data collection from different sources to pre-processing, data conversion, synchronization, and deep learning classification to estimate the total power use of the future process plan is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building power estimation models without manual data pre-processing. The proposed system is applied to a modular factory connected with machine controllers using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline with the result of the power profile synchronized with the robot program.

show abstract