scholarly journals Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Yiming Li
Keyword(s):  
Big Data ◽  
Scientific Research ◽  
Research Management ◽  
Support Vector ◽  
Optimization Strategy ◽  
Web Database ◽  
Computing Platform ◽  
Research And Innovation ◽  
Software Configuration ◽  
Big Data Applications

In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

scholarly journals Analyzing Big Data with the Hybrid Interval Regression Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chia-Hui Huang ◽  
Keng-Chieh Yang ◽  
Han-Ying Kao
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Information Technologies ◽  
Large Scale ◽  
Cloud Services ◽  
Support Vector ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Smooth Support Vector Machine ◽  
Scale Data

Big data is a new trend at present, forcing the significant impacts on information technologies. In big data applications, one of the most concerned issues is dealing with large-scale data sets that often require computation resources provided by public cloud services. How to analyze big data efficiently becomes a big challenge. In this paper, we collaborate interval regression with the smooth support vector machine (SSVM) to analyze big data. Recently, the smooth support vector machine (SSVM) was proposed as an alternative of the standard SVM that has been proved more efficient than the traditional SVM in processing large-scale data. In addition the soft margin method is proposed to modify the excursion of separation margin and to be effective in the gray zone that the distribution of data becomes hard to be described and the separation margin between classes.

scholarly journals Serverless Computing Platform for Big Data Storage

2019 ◽  
Vol 8 (3) ◽  
pp. 6592-6595
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Cloud Storage ◽  
Optimal Solution ◽  
Cloud Provider ◽  
Random Load ◽  
Data Movement ◽  
Computing Platform ◽  
Big Data Applications ◽  
Load Increase

This paper describes various challenges faced by the Big Data cloud providers and the challenges encountered by its users. This foreshadows that the Serverless computing as the feasible platform for Big Data application’s data storages. The literature research undertaken focuses on various Serverless computing architectural designs, computational methodologies, performance, data movement and functions. The framework for Serverless cloud computing is discussed and its performance is tested for the metric of scaling in the Serverless cloud storage for Big Data applications. The results of the analyses and its outcome are also discussed. Thus suggesting that the scaling of Serverless cloud storage for data storage during random load increase as the optimal solution for cloud provider and Big Data application user.

scholarly journals The dynamics of big data and human rights: the case of scientific research

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160129 ◽  
Author(s):  
Effy Vayena ◽  
John Tasioulas
Keyword(s):  
Human Rights ◽  
Big Data ◽  
Data Science ◽  
Scientific Research ◽  
Dynamic Interaction ◽  
Digital Environment ◽  
Big Data Applications ◽  
Health Related Research ◽  
Health Related ◽  
Over Time

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities—the new capabilities and risks—of the rapidly evolving digital environment. This article is part of the themed issue ‘The ethical impact of data science’.

Sign in / Sign up

Export Citation Format

Share Document