Towards Improved Data Analytics Through Usability Enhancement of Unstructured Big Data

Adnan, K; Akbar, Rehan; Wang, Khor Siak

doi:10.1109/iccoins49721.2021.9497187

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…descriptive approaches for data usability only handle structured data [6,7]. Further, the existing literature highlighted the significance of unstructured data usability during the preparation of data for analysis [2,3].…”

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confidence: 99%

Usability enhancement model for unstructured text in big data

Adnan,

Akbar,

Wang

2023

J Big Data

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The task of insights extraction from unstructured text poses significant challenges for big data analytics because it contains subjective intentions, different contextual perspectives, and information about the surrounding real world. The technical and conceptual complexities of unstructured text degrade its usability for analytics. Unlike structured data, the existing literature lacks solutions to address the usability of unstructured text big data. A usability enhancement model has been developed to address this research gap, incorporating various usability dimensions, determinants, and rules as key components. This paper adopted Delphi technique to validate the usability enhancement model to ensure its correctness, confidentiality, and reliability. The primary goal of model validation is to assess the external validity and suitability of the model through domain experts and professionals. Therefore, the subject matter experts of industry and academia from different countries were invited to this Delphi, which provides more reliable and extensive opinions. A multistep iterative process of Knowledge Resource Nomination Worksheet (KRNW) has been adopted for expert identification and selection. Average Percent of Majority Opinions (APMO) method has been used to produce the cut-off rate to determine the consensus achievement. The consensus was not achieved after the first round of Delphi, whereas APMO cut-off rate was 70.9%. The model has been improved based on the opinions of 10 subject matter experts. After second round, the analysis has shown majority agreement for the revised model and consensus achievement for all improvements that validate the improved usability enhancement model. The final proposed model provides a systematic and structured approach to enhance the usability of unstructured text big data. The outcome of the research is significant for researchers and data analysts.

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confidence: 99%

Usability enhancement model for unstructured text in big data

Adnan,

Akbar,

Wang

2023

J Big Data

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Comparative Analysis on Classification of Unstructured Text data and Specific Summarization

Bhat

2024

2024 Second International Conference on Advances in Information Technology (ICAIT)

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Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

Lee

Jeong

Jung

et al. 2022

JPM

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With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and usability are insufficient. This paper proposes a verification of de-identification techniques for personal healthcare information by considering data confidentiality and usability. Data are generated and preprocessed by considering the actual statistical data, personal information datasets, and de-identification datasets based on medical data to represent the de-identification technique as a numeric dataset. Five tree-based regression models (i.e., decision tree, random forest, gradient boosting machine, extreme gradient boosting, and light gradient boosting machine) are constructed using the de-identification dataset to effectively discover nonlinear relationships between dependent and independent variables in numerical datasets. Then, the most effective model is selected from personal information data in which pseudonym processing is essential for data utilization. The Shapley additive explanation, an explainable artificial intelligence technique, is applied to the most effective model to establish pseudonym processing policies and machine learning to present a machine-learning process that selects an appropriate de-identification methodology.

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Towards Improved Data Analytics Through Usability Enhancement of Unstructured Big Data

Cited by 3 publications

References 37 publications

Usability enhancement model for unstructured text in big data

Usability enhancement model for unstructured text in big data

Comparative Analysis on Classification of Unstructured Text data and Specific Summarization

Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values

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