Benefit From the Internet of Things Right Now by Accessing Dark Data

Gimpel, Gregory; Alter, Allan

doi:10.1109/mitp.2020.3025483

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Dark data often represents lost opportunities (e.g., revenue, products) for a business as data content and quality is unknown. Almost 90% of IoT data is dark data [83]. If computed promptly and presented as feedback to humans, data quality metrics can instill a company culture to use data before it becomes dark.…”

Section: M28mentioning

confidence: 99%

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

et al. 2023

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The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.

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Section: M28mentioning

confidence: 99%

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

et al. 2023

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“…Its emergence is considered significant for data keepers as it helps them to manage or even avoid the occurrences of dark data. While some may accept dark data as a harmful substance in the data repository (Hand, 2020;Munot et al (2019); Lugmayr et al, 2017), some are leveraging them for betterment (Almeida et al, 2021;Gimpel & Alter, 2021;Hawkins et al, 2020;Gimpel, 2020a). Benefiting from dark data requires the organization to manage and deal with dark data during pre-existence and/or post-existence.…”

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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“…Researchers have also discussed types of dark data by exploring hidden cybersecurity risks, and showed how companies can be proactive by managing the data in the right way to reduce those risks [24]. Researchers have also discussed the dark data in IoT devices and the use of AI in business analytics, and showed how businesses could benefit by unveiling trends which could increase productivity and efficiency [25,26]. Dark data has specific properties which companies could utilize to release its potential as a business asset if they set up business models for handling it [9].…”

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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Benefit From the Internet of Things Right Now by Accessing Dark Data

Cited by 5 publications

References 6 publications

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

Enlightening the Repercussion of Dark Data Management towards Malaysian SMEs Sustainability

A Novel Approach for Deciphering Big Data Value Using Dark Data

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