Millipede: Die-Stacked Memory Optimizations for Big Data Machine Learning Analytics

2018 ◽  
Author(s):  
. Nitin ◽  
Mithuna Thottethodi ◽  
T. N. Vijaykumar
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Analytics ◽  
Memory Optimizations ◽  
Stacked Memory

scholarly journals Machine Learning Based On Big Data Extraction of Massive Educational Knowledge

2017 ◽  
Vol 12 (11) ◽  
pp. 151 ◽  
Author(s):  
Abdelladim Hadioui ◽  
Nour-eddine El Faddouli ◽  
Yassine Benjelloun Touimi ◽  
Samir Bennani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Analytics ◽  
Data Extraction ◽  
Learning System ◽  
Massive Data ◽  
Data Usage ◽  
E Learning ◽  
Uniform Model ◽  
Educational Field

A learning environment generates massive knowledge by means of the services provided in MOOCs. Such knowledge is produced via learning actor interactions. This result is a motivation for researchers to put forward solutions for big data usage, depending on learning analytics techniques as well as the big data techniques relating to the educational field. In this context, the present article unfolds a uniform model to facilitate the exploitation of the experiences produced by the interactions of the pedagogical actors. The aim of proposing the said model is to make a unified analysis of the massive data generated by learning actors. This model suggests making an initial pre-processing of the massive data produced in an e-learning system, and it’s subsequently intends to produce machine learning, defined by rules of measures of actors knowledge relevance. All the processing stages of this model will be introduced in an algorithm that results in the production of learning actor knowledge tree.

Big Data Analyses in the Community of Inquiry and Educational Research Spheres

2018 ◽  
pp. 34-52
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Educational Research ◽  
Learning Analytics ◽  
Community Of Inquiry ◽  
Educational Data Mining ◽  
Data Analyses ◽  
Data Volume ◽  
Learning Data

Chapter 3 builds on the previous chapters and provides a summary of big data-style research within the Community of Inquiry scholarly literature, as well as examples from educational research broadly. This chapter also connects to the broader topics of machine learning, data analytics, learning analytics, and educational data mining. Constructs from the Community of Inquiry are integrated into this synthesis and overview. Unfortunately, only a fraction of the studies in educational research broadly today exhibit the tell-tale signs of big data: data volume and variety, new environments or instrumented sources of larger data, often with emerging tools and platforms critical to the analysis of the resulting datasets. A list of additional readings is provided.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

2020 ◽  
Author(s):  
Turan G. Bali ◽  
Amit Goyal ◽  
Dashan Huang ◽  
Fuwei Jiang ◽  
Quan Wen
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Corporate Bonds ◽  
Cross Sectional ◽  
The Cross

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

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