Bringing dark data into the light: Illuminating existing IoT data lost within your organization

Gimpel, Gregory

doi:10.1016/j.bushor.2020.03.009

Cited by 26 publications

(13 citation statements)

References 6 publications

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“…Often organizations generate much more data than they are able to interpret, and current Cloud Computing technologies cannot fully meet the requirements of the Big Data processing applications and their data transfer overheads [1]. Many data are stored for compliance purposes only but not used and turned into value, thus becoming Dark Data, which are not only an untapped value, but also pose a risk for organizations [3].…”

Section: Summary Of the Projectmentioning

confidence: 99%

Research Challenges in Information Science

2021

Lecture Notes in Business Information Processing

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Section: Summary Of the Projectmentioning

confidence: 99%

Research Challenges in Information Science

2021

Lecture Notes in Business Information Processing

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“…Although process monitoring and automatic control have become standard, for example in largest wastewater treatment systems, there is still a tendency to rely heavily on existing knowledge and empirically driven decision-making, where the plant operators are the experts, while much of the data gathered at the plant is unused. This has come to be known as 'dark data', which is data collected by an industry that is either archived or discarded in lieu of capacity or motivation for its use at that time [114]. It can be argued that for AD plants, dark data is largely irrelevant given the quantity of data typically acquired, but as industries are increasingly integrated, relying on automation and knowledge extracted from data, so the opportunities offered by digital technologies, which can manage large and diverse amounts of information, become apparent [115].…”

Section: Empiricism Data and The Digital Futurementioning

confidence: 99%

Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes

Wade

2020

Processes

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Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.

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“…While process monitoring and automatic control have become standard, for example in most large wastewater treatment systems, there is still a tendency to rely heavily on existing knowledge and empirically driven decision making, where the plant operators are the experts, while much of the data gathered at the plant is unused. This has come to be known as 'dark data', that is data collected by an industry that is either archived or discarded in lieu of capacity or motivation for its use at that time [111]. It can be argued that for AD plants, dark data is largely irrelevant given the quantity of data typically acquired, but as industries are increasingly integrated, relying on automation and knowledge extracted from data, so the opportunities offered by digital technologies, which can manage large and diverse amounts of information, become apparent [112].…”

Section: Empiricism Data and The Digital Futurementioning

confidence: 99%

Not Just Numbers: Mathematical Modelling and its Contribution to Anaerobic Digestion Processes

Wade¹

2020

Preprint

View full text Add to dashboard Cite

Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, still remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.

show abstract

Bringing dark data into the light: Illuminating existing IoT data lost within your organization

Cited by 26 publications

References 6 publications

Research Challenges in Information Science

Research Challenges in Information Science

Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes

Not Just Numbers: Mathematical Modelling and its Contribution to Anaerobic Digestion Processes

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