Affordances of Data Science in Agriculture, Manufacturing, and Education

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today’s unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

[1]  Mohammad Nazim,et al.  Implementing Knowledge Management In Indian Academic Libraries , 2011 .

[2]  Vishal Bhatnagar,et al.  Movie Analytics for Effective Recommendation System using Pig with Hadoop , 2016, Int. J. Rough Sets Data Anal..

[3]  M. P. S. Bhatia,et al.  An Approach to Distinguish Between the Severity of Bullying in Messages in Social Media , 2016, Int. J. Rough Sets Data Anal..

[4]  Arti Arun Mohanpurkar,et al.  A Traitor Identification Technique for Numeric Relational Databases with Distortion Minimization and Collusion Avoidance , 2016, Int. J. Ambient Comput. Intell..

[5]  Gholamreza Pezeshki Rad,et al.  Use of Computer and Internet in Agricultural Extension as Perceived by Extension Workers , 2014 .

[6]  Basma Mohammed Hassan,et al.  User Authentication based on Dynamic Keystroke Recognition , 2016, Int. J. Ambient Comput. Intell..

[7]  Nilanjan Dey,et al.  Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients , 2016, Applications of Intelligent Optimization in Biology and Medicine.

[8]  Vishal Bhatnagar,et al.  Olympics Big Data Prognostications , 2016, Int. J. Rough Sets Data Anal..

[9]  Vladimír Bures,et al.  Application of Ambient Intelligence in Educational Institutions: Visions and Architectures , 2016, Int. J. Ambient Comput. Intell..

[10]  Nilanjan Dey,et al.  Theoretical Analysis of Different Classifiers under Reduction Rough Data Set: A Brief Proposal , 2016, Int. J. Rough Sets Data Anal..

[11]  Kate Otto,et al.  ICTs for health in Africa , 2014 .

[12]  Naomi J. Halewood,et al.  Mobilizing the Agricultural Value Chain , 2012 .

[13]  H. Fisher.  Department for Business, Innovation & Skills , 2010 .

[14]  Carlos Eduardo Pereira,et al.  Manufacturing plant control: Challenges and issues , 2007 .

[15]  Agnes Grünerbl,et al.  UWB Indoor Location for Monitoring Dementia Patients: The Challenges and Perception of a Real-Life Deployment , 2013, Int. J. Ambient Comput. Intell..

[16]  Marie Bienkowski,et al.  Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief , 2012 .

[17]  Martin Ebner,et al.  Learning Analytics: Principles and Constraints , 2015 .

[18]  Juan M. Corchado,et al.  An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care , 2009, Int. J. Ambient Comput. Intell..

[19]  Sanjaya Kumar Panda,et al.  An Efficient Intra-Server and Inter-Server Load Balancing Algorithm for Internet Distributed Systems , 2017, Int. J. Rough Sets Data Anal..

[20]  Naomi J. Halewood,et al.  The Transformational Use of Information and Communication Technologies in Africa , 2012 .