A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations

Robertson, James; Fossaceca, John M.; Bennett, Kelly W.

doi:10.1109/tem.2021.3088382

Cited by 19 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blockchain technology refers to decentralized and distributed digital ledgers consisting of blocks where transactions and information are recorded using a peer-to-peer network to guarantee inalterability and security of data [14]. Computing technologies enclose both quantum and cloud-based computing, where the former refers to technologies allowing great computational power, whereas the latter regards a set of information technologies based on on-demand availability of resources, such as storage and computing, which are distributed over multiple locations [53]. The digital applications category contains software, social networks, and digital tools, accessible from smartphones and browsers, which run on web servers and support paperless activities [54].…”

Section: Table II Taxonomy Of Key Technologiesmentioning

confidence: 99%

A Framework for Investigating the Adoption of Key Technologies: Presentation of the Methodology and Explorative Analysis of Emerging Practices

Cammarano

Varriale

Michelino

et al. 2024

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

In this article, we present a framework for performing a systematic literature review on the implementation of emerging business practices and the adoption of these key technologies: three-dimensional printing, artificial intelligence, blockchain, computing, digital applications, geospatial technologies, immersive environments, Internet of things, open and crowd-based platforms, proximity technologies, and robotics. Through the content analysis of the full text of scientific papers, emerging practices are captured and then classified within a group of standardized labels. This allows to summarize an emerging practice as the use of a key technology within a business context-represented by industry, business function, and business process adopting the technology-with the aim of achieving improvements in terms of business performance. The adoption of this methodology allows the recognition of the opportunities for companies of enhancing performance through key technologies. With this study, the framework is tested by classifying more than 22 000 scientific articles and extracting 15 708 emerging practices. The explicative power of data collected is demonstrated by performing an explorative analysis aiming at illustrating the state-of-the-art of emerging practices. Moreover, by employing association rules algorithms, 262 practices are found as the most frequent and relevant and are the candidate for becoming future best practices.

show abstract

Section: Table II Taxonomy Of Key Technologiesmentioning

confidence: 99%

A Framework for Investigating the Adoption of Key Technologies: Presentation of the Methodology and Explorative Analysis of Emerging Practices

Cammarano

Varriale

Michelino

et al. 2024

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…• Cost Savings: Cloud computing allows businesses to just pay for the resources they utilise. This saves a significant amount of money compared to the typical infrastructure expenditures of building and maintaining massive data centres [60]. Saved money may be utilised to build more strategic AI tools and accelerators, which can then be used to increase revenue and save the company money at the core [61].…”

Section: Nextmentioning

confidence: 99%

AI for Next Generation Computing: Emerging Trends and Future Directions

Gill,

Xu,

Ottaviani

et al. 2022

Preprint

View full text Add to dashboard Cite

Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

show abstract

“…The deployment of an AI/ML Product reproduces the experimental design with additional requirements related to the quality assurance of the produced insight. The development of these DataOps and MLOps solutions follow the Agile methodology [25,31]. Since the solution architectures for MLOps are getting more established, there is a trend to build reusable elements with so-called design patterns [32].…”

Section: Software Solution Architecturementioning

confidence: 99%

“…This experimental design is critical during the development cycle. Practical experimentation enables researchers to save time and cost during AI/ML Product development [25]. An experimental design represents three main steps (see Figure 1): (1) data: in order to perform practical experimentation and training of the algorithm, the quality of the data is fundamental (often referred to as garbage-in garbage-out) [26], (2) AI/ML Product: the algorithm learns the optimal parameters during training, and (3) insight: the prediction that enables the business practitioners to make better decisions and evaluated by performance metrics.…”

Section: Introductionmentioning

confidence: 99%