Relevant bibliographies by topics / IBM Intelligent Operations Center

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Academic literature on the topic 'IBM Intelligent Operations Center'

Author: Grafiati
Published: 24 February 2023
Last updated: 30 July 2024

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Journal articles on the topic "IBM Intelligent Operations Center"

1

Butner, Karen, and Grace Ho. "How the human-machine interchange will transform business operations." Strategy & Leadership 47, no. 2 (2019): 25–33. http://dx.doi.org/10.1108/sl-01-2019-0003.

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Purpose Machine learning is beginning to transform the way businesses organize their operations and benefit from technology investments. Design/methodology/approach To learn more about how far along organizations are in deploying intelligent automation and in developing plans and strategies for its adoption, the IBM Institute for Business Value, in collaboration with Oxford Economics surveyed and interviewed 550 technology and operations executives. Findings The primary purpose of intelligent automation is to augment employees’ skills, experience and expertise, extending the human mind in ways that allow for higher productivity, creative problem-solving and more engaging jobs for employees. Practical implications Automation is not a plug-and-play solution: companies cannot just buy the technology, flip the switch and watch robots run the business without any human intervention. Originality/value This recent survey of operations executive with specific knowledge of their companies plans provides insights into best practice. Executives believe that layering new technologies on top of old business processes is apt to be less productive ? and less cost-effective ? than rethinking processes to make the most of intelligent automation. Executives must optimize workflows for automation; this means envisioning the end result, enabling it through logical steps and prototyping the process ? then repairing as necessary before scaling.
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Zhang, Yiyun. "Large Language Model in SD-WAN Intelligent Operations and Maintenance." Research Briefs on Information and Communication Technology Evolution 9 (December 19, 2023): 178–88. http://dx.doi.org/10.56801/rebicte.v9i.177.

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The integration of LLM (Large Language Model) into the intelligent operations and maintenance of SD-WAN (Software-Defined Wide Area Networks) is comprehensively explored. As the scale of the network continues to expand and its complexity increases, traditional operation and maintenance methods can no longer meet efficient and accurate requirements. Therefore, LLM technology was introduced, and the intelligent operation and maintenance of the data center network were realized by leveraging its powerful natural language processing capabilities. The importance and shortcomings of SD-WAN and the application scenarios of LLM in network operation and maintenance are first introduced, followed by an elaboration on how LLM can be used for network configuration, network optimization, network maintenance, and network security risk management. Through specific analysis, the significant effect of LLM in enhancing the efficiency and quality of operation and maintenance has been demonstrated. Finally, the advantages and challenges of employing LLM for intelligent network operation and maintenance are summarized, and future research directions are anticipated.
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3

Lei, Nuoa, Zhu Cheng, Zhi Cao, and Eric Masanet. "An integrated decision-making framework for sustainable data center operation through intelligent load scheduling." Journal of Physics: Conference Series 2042, no. 1 (2021): 012091. http://dx.doi.org/10.1088/1742-6596/2042/1/012091.

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Abstract Intelligent load scheduling is an emerging approach that has the potential to facilitate extreme sustainable data center (DC) operation. However, scarcity of straightforward tools in the public domain challenges decision makers performing quantitative analysis of the DC load planning and its potential benefits. In this work, a novel integrated decision-making framework was developed to address this issue, which provides the basis for the multi-objective optimization of carbon-, water-, and economic-intelligent load scheduling. The proposed framework was demonstrated with a case study DC in California, which showed the usefulness of the proposed framework in informing sustainable DC operations.
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Brydia, Robert E., Shawn M. Turner, William L. Eisele, and Jyh C. Liu. "Development of Intelligent Transportation System Data Management." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (1998): 124–30. http://dx.doi.org/10.3141/1625-16.

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The intelligent transportation system (ITS) components deployed in U.S. urban areas produce vast amounts of data. These ITS data often are used for real-time operations and then are discarded. Few transportation management centers have any mechanism for sharing the data resources among other transportation groups or agencies within the same jurisdiction. Meanwhile, transportation analysts and researchers often struggle to obtain accurate, reliable data about existing transportation performance and patterns. The development of an ITS data management system (referred to as ITS DataLink) that is used to store, access, analyze, and present data from the TransGuide center in San Antonio, Texas, is presented. Data outputs are both tabular and graphical. No user costs are associated with the system except for an Internet connection.
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5

Tong, Xin Shun, and Li Hua Yang. "Research on Integrated Scheduling Model of Automated High-Rise Warehouse and Traditional Flat Storehouse for Cigarette Distribution Center." Applied Mechanics and Materials 65 (June 2011): 99–103. http://dx.doi.org/10.4028/www.scientific.net/amm.65.99.

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Along with the market demand increasing, cigarette distribution center urgently needs to improve their working efficiency by reasonable storage modeal. This paper proposed an integrated scheduling model of automated high-rise warehouse combined with traditional flat storehouse, by planning the storage capacity in traditional flat storehouse and optimizing the storage spaces in automated high-rise warehouse, ensuring intelligent management while achieving flexible storage operations. This model effectively shortened the operating time compared to other storage models. Finally take Zhengzhou Cigarette Distribution Center as an example to analyze.
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Tavana, Madjid. "Intelligent flight support system (IFSS): a real-time intelligent decision support system for future manned spaceflight operations at Mission Control Center." Advances in Engineering Software 35, no. 5 (2004): 301–13. http://dx.doi.org/10.1016/j.advengsoft.2004.04.002.

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7

Epicoco, I., S. Mocavero, F. Macchia, et al. "Performance and results of the high-resolution biogeochemical model PELAGOS025 within NEMO." Geoscientific Model Development Discussions 8, no. 12 (2015): 10585–625. http://dx.doi.org/10.5194/gmdd-8-10585-2015.

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Abstract. The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. PELAGOS025 is an on-line coupling between the physical ocean model NEMO and the BFM biogeochemical model. Both the models use a parallel domain decomposition along the horizontal dimension. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach.
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Epicoco, Italo, Silvia Mocavero, Francesca Macchia, et al. "Performance and results of the high-resolution biogeochemical model PELAGOS025 v1.0 within NEMO v3.4." Geoscientific Model Development 9, no. 6 (2016): 2115–28. http://dx.doi.org/10.5194/gmd-9-2115-2016.

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Abstract. The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. PELAGOS025 is an on-line coupling between the Nucleus for the European Modelling of the Ocean (NEMO) physical ocean model and the Biogeochemical Flux Model (BFM) biogeochemical model. Both the models use a parallel domain decomposition along the horizontal dimension. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at the CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach.
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9

Donkoh, Elvis Kobina, Rebecca Davis, Emmanuel D. J. Owusu-Ansah, Emmanuel A. Antwi, and Michael Mensah. "Application of Combinatorial Techniques to the Ghanaian Board Game Zaminamina Draft." European Journal of Pure and Applied Mathematics 12, no. 1 (2019): 159–75. http://dx.doi.org/10.29020/nybg.ejpam.v12i1.3308.

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Games happen to be a part of our contemporary culture and way of life. Often mathematical models of conflict and cooperation between intelligent rational decision-makers are studied in these games. Example is the African board game ’Zaminamina draft’ which is often guided by combinatorial strategies and techniques for winning. In this paper we deduce an intelligent mathematical technique for playing a winning game. Two different starting strategies were formulated; center starting and edge or vertex starting. The results were distorted into a 3x3 matrix and elementary row operations were performed to establish all possible wins. MatLab was used to distort the matrix to determine the diagonal wins. A program was written using python in artificial intelligence (AI) to help in playing optimally
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10

Xiao-wei, Xiong. "Study on the intelligent system of sports culture centers by combining machine learning with big data." Personal and Ubiquitous Computing 24, no. 1 (2019): 151–63. http://dx.doi.org/10.1007/s00779-019-01307-z.

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AbstractWith the vigorous development of sports, people’s awareness of engaging in sports has gradually increased, and the requirements for a sports culture center have been higher. However, the service system of traditional sports cultures center is single, which cannot meet people’s growing experience needs. Therefore, it is urgent for the service system of sports culture centers to move towards intellectualization. Firstly, this paper discusses the service system of traditional sports culture centers and finds that there are some problems, such as slow transmission of information, poor sharing of resources, and weak flexibility of response, which seriously affect the consumer experience of users and restrict the development of sports culture centers. Then, with the help of computer network technology, the design of intelligent system architecture of sports culture centers is completed, which makes many intelligent subsystems interconnected and interoperable, integrates information, realizes the integration of data application network, and achieves the goal of resource sharing and function upgrading. Then, based on the intelligent system, the big data platform is built with the help of big data technology, and the support vector machine-back propagation (SVM-BP) neural network composite model is used to realize the prediction of the passenger flow in the cultural center, which provides guidance for adjusting the service plan in advance, effectively coping with the peak passenger flow and improving the user experience. Finally, through empirical analysis, we know that the design of an intelligent system greatly improves the service quality of cultural centers. The research results not only achieve a significant increase in passenger flow but also provide an effective way for the service of sports culture centers to move towards intellectualization.
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