Online ISSN: 2515-8260

Keywords : Big Data


European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 986-996

In general computing theory, Array is one of the data structures to hold a group of same data type elements.  Static array holds the underlying elements in an initial allocated memory space.  In contrast, dynamic array grows the memory allocation automatically and dynamically based on the insertion request volume.  Our research work is to leverage dynamic memory overlay array mechanism in Enterprise Data Hub.  It is continuation of our earlier work on Hybrid LRU Algorithm, which depicts about the experimental advantage of execution time optimization and efficient page/cache hit ratio, using hybrid Least Recently Used algorithm with priority mechanism.  With the ability to change the allocated space dynamically using memory overlay concept, Dynamic array improves the storage scalability and faster execution.

Applicationsof Big Dataand Analytics In Higher Education-A Review

Dr.S.Albert Antony Raj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 5153-5162

Usage of emerging technologies become inevitable for creating business value and to swim across the challenges in a digitally driven world. This is applicable in the field of higher education as well. Generally data analytics may enable to get more insights to obtain a better understanding about the needs and wants of all stakeholders of higher education. Higher education experiences only limited progress in hoardingreally rich data that flow through higher education.The objective of this review is to assess some of the prospective benefits of big data and analytics as applied to the world ofhigher education and to explore implementation challenges that can be expected. In addition, thisstudy reviews keypropertiesof successful analytics platforms and elucidates some of the routesthat might be taken to implement thesetechnologies in education.

An Empirical Study of Deep Learning Strategies for Spatial Data Mining

K. Sivakumar; A.S. Prakaash

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5124-5132

The emergence of scalable frameworks for machine learning to efficiently analyse and derive valuable insights from these data has triggered growing volumes of data collected. Huge spatial data frameworks cover a wide variety of priorities, including tracking of infectious diseases, simulation of climate change, etc. Conventional mining techniques, especially statistical frameworks to handling these data, are becoming exhausted due to the rise in the number, volume and quality of spatio-temporal data sets. Various machine learning tasks have recently shown efficiency with the development of deep learning methods. We therefore include a detailed survey in this paper on important impacts in the application of deep learning techniques to the mining of spatial data.