Integrating Data Mining and Data Management Technologies for Scholarly Inquiry

Larson, Ray R.; Marciano, Richard; Hou, Chien-Yi; Shreyas,; Watry, Paul; Harrison, John; Aguilar, L. A.; Fuselier, Jérôme

doi:10.1109/bigdata.2014.7004455

Cited by 3 publications

(2 citation statements)

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“…[36][37][38][39] finally, text mining is another frequently used approach for educational analytics e.g. [40][41][42][43][44]. Ifenthaler [45][46] have carried out study towards proving that all the upcoming forms of education system will be requiring advanced forms of analytics.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Framework for Big Data Analytics in Education

Shidaganti¹,

Prakash²

2021

IJACSA

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With the adoption of cloud services for hosting knowledge delivery system in educational domain, there is a surplus quantity of education data being generated every day by current learning management system. Such data are associated with certain typical complexities that impose significant challenges for existing database management and analytics. Review of existing approaches towards educational data highlights that they do not offer full-fledged solution towards analytics and still there is an open-end problem. Therefore, the proposed system introduces a comprehensive framework which offers integrated operation of transformation, data quality, and predictive analytics. The emphasis is more towards achieving distributed analytical operation towards educational data in cloud. Implemented using analytical research methodology, the proposed system shows better analytical performance with respect to frequently used educational data analytical approaches.

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Section: Related Workmentioning

confidence: 99%

A Comprehensive Framework for Big Data Analytics in Education

Shidaganti¹,

Prakash²

2021

IJACSA

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“…The integration of data mining and data management technologies for scholarly inquiries becomes a common practice with major effects in terms of increased role of team organization, augmented levels of coauthorship, distributed interactions and cooperation among scholars located in remote locations (Cobo and Naval, 2013). There is a strong consensus that the consequences are positive in terms of increased efficiency in the generation of scientific knowledge stemming from higher levels of division of labor and specialization (Hamermesh and Oster, 1998;Larson, R.R. et alii, 2014).…”

Section: Ict: Knowledge Spillovers and Absorption Costsmentioning

confidence: 99%

Digital knowledge generation and the appropriability trade-off

Antonelli

2017

Telecommunications Policy

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The introduction of information and communication technologies (ICT) has changed in depth the organization of the generation of knowledge reducing significantly knowledge absorption cost and improving knowledge interactions. The digital generation of knowledge relies on the systematic access and use of the stock of quasi-public knowledge. ICT enable to reconsider the knowledge appropriability trade-off as it helps to better appreciate the positive role of knowledge spillovers in the recombinant generation of new knowledge, next to the well-known negative effects of the limited appropriability of knowledge on revenues and hence incentives to innovate. This new analytical framework calls for an augmented role of telecommunications policy that should take into account the positive effects of knowledge connectivity on the generation of knowledge.

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Analysis and Research of Students’ Mental Health Status Based on K-Means Clustering Under the Background of Big Data

Liu

et al. 2021

Lecture Notes on Data Engineering and Communications Technologies

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Integrating Data Mining and Data Management Technologies for Scholarly Inquiry

Cited by 3 publications

References 1 publication

A Comprehensive Framework for Big Data Analytics in Education

A Comprehensive Framework for Big Data Analytics in Education

Digital knowledge generation and the appropriability trade-off

Analysis and Research of Students’ Mental Health Status Based on K-Means Clustering Under the Background of Big Data

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