Big Data Characteristics (V’s) in Industry

Saeed, Nagham; Husamaldin, Laden

doi:10.53523/ijoirvol8i1id52

Cited by 8 publications

(8 citation statements)

References 17 publications

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“…Timeline depicting major historical events throughout classification in chemistry, chemical data management, and cheminformatics. ,− …”

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

“…The problems of biased training data producing biased ML algorithms and, more generally, analysis of biased data producing biased results are well documented across the natural and social sciences. − Misleading results will not only confuse and distract scientific progress but also undermine public confidence in science itself. − To manage difficulties associated with big data, data scientists have developed several metrics for assessing the reliability and accessibility of results from big data use. The “five Vs” − velocity, volume, value, variety and veracityserve as use principles for generating reliable results from big data (Figure ). According to the five Vs, large quantities (volume) of reliable (veracity) and diverse (variety) data should be produced and analyzed quickly (velocity) and yield meaningful insights (value).…”

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

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Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Duke,

McCoy,

Risko

et al. 2024

J. Am. Chem. Soc.

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Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.■ CHEMISTRY'S BIG DATA ERA As with most sciences in the 21st century, chemistry is witnessing a seismic shift in data collection, storage, and use. The collection of large volumes of data (so-called "big data") for the purpose of using computer algorithms to aid in chemical discovery is becoming commonplace, and many experiments produce more raw data than any one scientist could review in a lifetime. Moreover, federal funding agencies now require extensive data management plans and that data be made open under FAIR (f̲ indable, a̲ ccessible, i̲ nteroperable, and r̲ eusable) 1 data principles. In the United States, the National Science Foundation, National Institutes of Health, and Department of Energy among others have begun requiring data management plans for proposal applications, 2−4 while similar efforts exist globally to promote robust and accessible research data management. 5,6 Along with funding agencies, researchers across the globe are pushing for accessible, usable, and reliably reproducible data in chemistry. 7−9 Many argue that this new era of data will transform modern chemistry from an Edisonian model of trial-and-error to data-driven and machine-enabled design through artificial intelligence (AI). 10,11 Chemical Data: Historical Background. Although the modern era of big data is still incipient, data hold an integral role in the history of chemistry (Figure 1). 12−14 Chemists have long prioritized documenting and sharing thei...

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“…Timeline depicting major historical events throughout classification in chemistry, chemical data management, and cheminformatics. ,− …”

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

Section: Chemistry’s Big Data Eramentioning

confidence: 99%

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Duke,

McCoy,

Risko

et al. 2024

J. Am. Chem. Soc.

View full text Add to dashboard Cite

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“…Layer 4 (Data Accumulation): The layer stores and manages the big data obtained from the physical devices and controllers in Layer 1. The offshore wind farm's dataacquisition system's attributes are variety, which "refers to the different types of structured and unstructured data such as text, sensor data, audio, video, and graph from heterogeneous data sources" [39]; velocity, which "refers to how quickly the data can be analyzed to make decisions or to obtain useful analytic information in real-time" [40]; veracity, which refers to "the reliability and insightfulness of data" [41], and volume, which "refers to the large amount of data that grows continuously as more data is obtained from the IIoT devices mounted on the offshore wind farm's critical infrastructure" [40].…”

Section: Industry 40 Iiot Cloud and Edge Computing For Next-generatio...mentioning

confidence: 99%

“…Data-at-Rest (DAR) Module: The data-at-rest (DAR) module comprises Layer 5 to Layer 7. Layer 5 (Data Abstraction): This layer aggregates the data and simplifies how the underlying data are represented, abstracting the end users from unnecessary background details of the heterogeneous data using object-oriented programming and other database management tools [40,41]. Layer 6 (Application): This layer comprises software applications built on the microservices architecture that performs monitoring, visualization, predictive analytics, forecasting, trends, and control logic using application programming interfaces to interact with the data stored in Layer 7 [39].…”

Section: Industry 40 Iiot Cloud and Edge Computing For Next-generatio...mentioning

confidence: 99%

Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

Mwangi,

Sahay,

Fumagalli

et al. 2024

Energies

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Offshore wind farms are growing in complexity and size, expanding deeper into maritime environments to capture stronger and steadier wind energy. Like other domains in the energy sector, the wind energy domain is continuing to digitalize its systems by embracing Industry 4.0 technologies such as the Industrial Internet of Things (IIoT), virtualization, and edge computing to monitor and manage its critical infrastructure remotely. Adopting these technologies creates dynamic, scalable, and cost-effective data-acquisition systems. At the heart of these data-acquisition systems is a communication network that facilitates data transfer between communicating nodes. Given the challenges of configuring, managing, and troubleshooting large-scale communication networks, this review paper explores the adoption of the state-of-the-art software-defined networking (SDN) and network function virtualization (NFV) technologies in the design of next-generation offshore wind farm IIoT–Edge communication networks. While SDN and NFV technologies present a promising solution to address the challenges of these large-scale communication networks, this paper discusses the SDN/NFV-related performance, security, reliability, and scalability concerns, highlighting current mitigation strategies. Building on these mitigation strategies, the concept of resilience (that is, the ability to recover from component failures, attacks, and service interruptions) is given special attention. The paper highlights the self-X (self-configuring, self-healing, and self-optimizing) approaches that build resilience in the software-defined IIoT–Edge communication network architectures. These resilience approaches enable the network to autonomously adjust its configuration, self-repair during stochastic failures, and optimize performance in response to changing conditions. The paper concludes that resilient software-defined IIoT–Edge communication networks will play a big role in guaranteeing seamless next-generation offshore wind farm operations by facilitating critical, latency-sensitive data transfers.

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“…This data can contain significant informational value whereby its use grows in proportion to the increasing trend of data being generated daily. (Husamaldin, Saeed, 2021) Big Data represent a loosely defined term that describes large quantity of complex data sets and, at the same time, the advanced technologies for collection and storage of such large data quantities (Keen, 1991 There are many definitions of Big Data, but there is no single and exact one. Definitions differ, representing different opinions of various authors.…”

Section: Big Data As the Platform For The Company's Decision-making P...mentioning

confidence: 99%

Business-making supported via the application of big data to achieve economic sustainability

Koman¹,

Tumová²,

Jankal³

et al. 2022

JESI

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Decision-making of managers in companies is currently significantly influenced by instantly available information. This phenomenon was caused by advances in information and communication technologies (ICT) and the development of a new generation of machines and systems that create an abundance of diverse data. For managers, this data is a source of information that needs to be obtained in an appropriate manner using different technologies. Technologies that enable the acquisition of this information from available data become an essential part of each company and its management processes. Since technology is currently entering the decision-making process in a significant way, this can be seen as the digital transformation of decision-making processes in companies. The ability to capture, process and assess a very large amount of available data represents the implementation of decisions that make the company's activity more efficient and sustainable (e.g., achieving a competitive advantage, increasing sales or profits, development of new products, etc.). The article presents the results of the conducted research focused on identifying the benefits of implementing Big Data solutions in companies. The main method applied was content analysis of multiple cases followed by the categorization of data extracted. The research hypothesis was stated as follows: There are significant reasons that justify the application of Big Data in managerial decision-making to support the achievement of economic sustainability. The findings from the research (categories) support the importance of the Big Data solutions applied in the managerial decision-making for ensuring the sustainable operation of companies with the current trend of generating and processing a large variety of data.

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Big Data Characteristics (V’s) in Industry

Cited by 8 publications

References 17 publications

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Towards a Software-Defined Industrial IoT-Edge Network for Next-Generation Offshore Wind Farms: State of the Art, Resilience, and Self-X Network and Service Management

Business-making supported via the application of big data to achieve economic sustainability

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