Big Data Optimization Techniques: A Survey

Roy, Chandrima; Rautaray, Siddharth Swarup; Pandey, Manjusha

doi:10.5815/ijieeb.2018.04.06

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…Even though they are amalgamated under one umbrella term, the corresponding endeavors comprise a variety of highly different use cases [1]. Common to them, however, is a strong focus on the scalability and portability of the deployed solutions [11][12][13]. Furthermore, in many scenarios, a high degree of flexibility is desirable [1], which also affects the design and development of those solutions.…”

Section: Big Datamentioning

confidence: 99%

Facing Big Data System Architecture Deployments: Towards an Automated Approach Using Container Technologies for Rapid Prototyping

Volk¹,

Staegemann²,

Islam³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Within the last decade, big data became a promising trend for many application areas, offering immense potential and a competitive edge for various organizations. As the technical foundation for most of today´s data-intensive projects, not only corresponding infrastructures and facilities but also the appropriate knowledge is required. Currently, several projects and services exist that not only allow enterprises to utilize but also to deploy related technologies and systems. However, at the same time, the use of these is accompanied by various challenges that may result in huge monetary expenditures, a lack of modifiability, or the risk of vendor lock-ins. To overcome these shortcomings, in the contribution at hand, modern container and task automation technologies are used to wrap complex big data technologies into re-usable and portable resources.

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Section: Big Datamentioning

confidence: 99%

Facing Big Data System Architecture Deployments: Towards an Automated Approach Using Container Technologies for Rapid Prototyping

Volk¹,

Staegemann²,

Islam³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…note, that these methods were applied for design of a many-links manipulator and of a car for shock protection. for example, they may be applied to the systemtems described in [3][4][5].…”

Section: Technical Sciencesmentioning

confidence: 99%

Vector Optimization of a Соmplicated Intellectual and Mechanical Systems

Matusov¹

2019

EJNH

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the design and optimization of a complicated systems are the most difficult problems. a complicated system consists of a number of subsystems and large set of a design variables or the calculation of one solution of such system can take very much of computer time. different departments of the design office engaged in creating a intellectual system (machine) optimize their 'own' subsystems, while ignoring others. a system assembled from 'autonomously optimal' subsystems turns out to be far from perfect. a intellectual system (machine) is a single whole. When improving one of its subsystems, we can unwittingly worsen others. this implies that it is not always possible to solve optimization problems directly even for determination of the feasible solution set. the correct determination of the feasible solution set was a major challenge in engineering optimization problems. in order to construct the feasible solution set, a method called the parameter Space investigation (pSi) has been created and successfully integrated into various fields of industry, science, and technology. the methods of approximation of the feasible solution and pareto optimal sets and the regularization of the pareto optimal set are described in our paper. these methods are applied for solving the multicriteria optimization problems of a complicated systems.

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“…In the article proposed in [28] to use general machine learning approach by finding that memory mapping is also a feasible technique for scaling up general machine learning algorithms that enables existing machine learning algorithms to work with out-of-core datasets through memory mapping a faster speed. A survey of latest optimization methods for big data applications is presented in [29]. Revision of data mining algorithms used for CRM is presented in review paper [30].…”

Section: B Related Work Reviewmentioning

confidence: 99%

Artificial Intelligent Machine Learning and Big Data Mining of Desert Geothermal Heat Pump: Analysis, Design and Control

Shibli¹,

Mathew²

2019

IJISA

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Nowadays sustainable underground geothermal energy resources have received special attention thanks for being characterized as clean, zerocarbon footprint, reliable, and free source of renewable energy that can run all year long and around the clock. Barren desert lands, which make up 33% and contribute to almost 30 Million km² of global land surface area, is increasingly seen as supply of green energy but not yet efficiently and globally utilized although it can save up to 70% compared to traditional HVAC systems bills. This paper presents a novel artificial intelligent machine learning and big data algorithm to analyze and control geothermal heat pump system (GHP). In particular, the main objective of this research is to model, design, analyze, control and optimize the performance of desert underground GTH system based on thermodynamics laws and AI machine learning. As a case study, the analysis and design of desert GHP is performed based on the annual weather data collected for Al Ain city in UAE. By selecting a horizontal layout, the design analysis results show that GHP unit needs a 66 m total trench length with a cooling capacity estimated of 12.4 kW, heat pump COP of 2.8 and 1.6 for the system COP with 30.3 L/min water flow rate. Similar results for the heating system are obtained as well. Furthermore, financial calculations show the GHP system is very economic and competitive comparing with the traditional cooling/heating systems. It

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Big Data Optimization Techniques: A Survey

Cited by 14 publications

References 21 publications

Facing Big Data System Architecture Deployments: Towards an Automated Approach Using Container Technologies for Rapid Prototyping

Facing Big Data System Architecture Deployments: Towards an Automated Approach Using Container Technologies for Rapid Prototyping

Vector Optimization of a Соmplicated Intellectual and Mechanical Systems

Artificial Intelligent Machine Learning and Big Data Mining of Desert Geothermal Heat Pump: Analysis, Design and Control

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