A Framework for Image Dark Data Assessment

Liu, Yu; Wang, Yangtao; Zhou, Ke; Yu-juan, Yang; Liu, Yifei; Song, Jingkuan; Zhang, Xiao

doi:10.1007/978-3-030-26072-9_1

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The distinction between structured and unstructured data does not prevent unused data from residing within the owner's repository. However, Liu et al (2019) and Imdad et al (2020) mentioned that the causal factor of data is unused datasets due to the unstructured format of data. The missing meaning of the unstructured data that lies deep within it, which requires to be extracted, such as images, videos, and audio, makes the data to be left unused, but its unknown potential value makes them to be kept longer.…”

Section: Literature Reviewmentioning

confidence: 99%

Enlightening the Repercussion of Dark Data Management towards Malaysian SMEs Sustainability

Md Ajis,

Md Jali,

Ishak

et al. 2023

E-BPJ

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The sheer volume of dark data impacts the costs for searching and producing appropriate information and imposes a wasted storage cost in operating budgets. Therefore, a grounded theory research was conducted to investigate the dark data phenomenon towards SMEs in Malaysia. Straussian Grounded Theory Methodology was deployed to analyze collected qualitative data to investigate the repercussions of dark data management towards sustainability of Small & Medium enterprises in Malaysia. Consequently, the study found that dark data is a precious asset to leverage and maintain sustainable business, and a model on the repercussions of dark data management was proposed.

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Section: Literature Reviewmentioning

confidence: 99%

Enlightening the Repercussion of Dark Data Management towards Malaysian SMEs Sustainability

Md Ajis,

Md Jali,

Ishak

et al. 2023

E-BPJ

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“…Business systems store data that is considered to be unproductive and without value, but there are various productive ways of using it if companies implement strategies to connect, organize, and analyze this dark data [19]. The appropriate analysis can show hidden patterns missed during conventional data analytics [10][11][12][13][14][15][16][17][18][19][20][21]. Munot et al [22] explained the importance of dark data and discussed applications for its analysis.…”

Section: Input Outputmentioning

confidence: 99%

“…In an automated payment process, information related to customers can be verified and checked from invoices at the first stage of the process so that data be prevented from being converted into dark data. If the data is not analyzed, it will have cost implications as regards storage [19]. Data-driven companies need to train employees adequately so that meaningful insights can be extracted.…”

Section: Input Outputmentioning

confidence: 99%

A Novel Approach for Deciphering Big Data Value Using Dark Data

Bhatia¹,

Alojail²

2022

Intelligent Automation &Amp; Soft Computing

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The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in 'dark data', data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined for practical purposes and utilized to gain business insight. The most common type of dark data is a log file generated on a web server. Using the example of log files generated by e-commerce transactions, this paper shows how residual data and data trails can prove to be valuable when an actual dataset is inaccessible, and explains the usage of residual data for modeling purposes. The work uses a system identification approach, based on natural language processing for log file tokenization and feature extraction. The features are then embedded into the next step, which uses a deep neural network to identify customers for targeted advertising. The results achieve a significant accuracy and show how dark data has the potential to deliver value for business. Locating, organizing, and understanding dark data can unlock its relevance, usefulness, and potential monetization, but it is important to act when the benefits of use outweigh the costs of access and analysis.

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