Data quality challenges in large-scale cyber-physical systems: A systematic review

Alwan, Ahmed Abdulhasan; Ciupala, Mihaela Anca; Brimicombe, Allan J.; Ghorashi, Seyed Ali; Baravalle, Andres; Falcarin, Paolo

doi:10.1016/j.is.2021.101951

Cited by 29 publications

(11 citation statements)

References 90 publications

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“…Collaboration between academics and practitioners is required to establish and execute high data quality standards. Addressing challenges of accuracy, consistency, and dependability is critical to realizing the full potential of open data [22]. This collaborative approach between researchers and data custodians is critical for defining and upholding standards that improve the integrity and usefulness of open data, resulting in better informed decision-making processes [23].…”

Section: Literature Reviewmentioning

confidence: 99%

The Improvement of the Use of Open Data in Public Institutions

Hyseni,

Bexheti

2024

Preprint

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The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

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Section: Literature Reviewmentioning

confidence: 99%

The Improvement of the Use of Open Data in Public Institutions

Hyseni,

Bexheti

2024

Preprint

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“…The existing research efforts revealed numerous successes in the application of DNNs, including anomaly detection [27,28], resource management [24], predictive maintenance [29][30][31][32], multi-modal data fusion [33], real-time decision making [34,35], and spatial-temporal data processing [36,37], among others. For example, Luo et al [28] reviewed the applications of deep learning for anomaly detection in CPS, outlining areas, where deep learning has achieved promising results and areas that need improvement.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

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Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

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“…According to International Organization for standardization, mapping geographical data quality comprises categories such as completeness, logical consistency, positional and thematical accuracy, and temporal quality [19]. Providing a community data quality map is crucial before analyzing results using GIS [20], but recent literature reviews stated that it is challenging [21,22], more importantly for our country, Indonesia. A data quality map in nutrition is most commonly derived from regional or national anthropometric data but lacks household data [23].…”

Section: Introductionmentioning

confidence: 99%

Posyandu Application for Monitoring Children Under-Five: A 3-Year Data Quality Map in Indonesia

Rinawan

Faza

Susanti

et al. 2022

IJGI

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Posyandu is an Indonesian mother-child health, community-based healthcare. The provision of the Posyandu data quality map is crucial for analyzing results but is limited. This research aimed to (a) demonstrate data quality analysis on its completeness, accuracy, and consistency and (b) map the data quality in Indonesia for evaluation and improvement. An observational study was conducted using the Posyandu application. We observed data in Indonesia from 2019 to 2021. Data completeness was identified using children’s visits/year. Data accuracy was analyzed using WHO anthropometry z-score and implausible z-score values analyzing the outliers. Cronbach’s α of variables was used to know data consistency. STATA 15.1 SE and QGIS 3.10 was used to analyze and map the quality. Data completeness and accuracy in three years show a good start for the pilot project area, continued with declines in pandemic time, while some other areas demonstrated a small start, then slightly increased. The overall consistency decreased through the study period. A good report on data completeness can occur initially in a pilot project area, followed by others. Data accuracy and consistency can decrease during the pandemic. The app can be promising when synchronized with the government health information system.

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Data quality challenges in large-scale cyber-physical systems: A systematic review

Cited by 29 publications

References 90 publications

The Improvement of the Use of Open Data in Public Institutions

The Improvement of the Use of Open Data in Public Institutions

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Posyandu Application for Monitoring Children Under-Five: A 3-Year Data Quality Map in Indonesia

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