scholarly journals A Big Data Platform for heterogeneous data collection and analysis in large-scale data centres

2021 ◽  
Author(s):  
Simone Rossi Tisbeni ◽  
Daniele CESINI ◽  
Barbara Martelli ◽  
Arianna Carbone ◽  
Claudia Cavallaro ◽  
...  
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Large Scale Data ◽  
Data Collection And Analysis ◽  
Data Platform ◽  
Data Centres ◽  
Scale Data

scholarly journals Mass customization capability

2013 ◽  
Vol 16 (2) ◽  
pp. 5-15
Author(s):  
Toan Khanh Vo ◽  
Hung Nguyen Bui
Keyword(s):  
Pilot Study ◽  
Data Collection ◽  
Mass Customization ◽  
Large Scale ◽  
Supplier Integration ◽  
Large Scale Data ◽  
Data Collection And Analysis ◽  
Product Modularity ◽  
Initial Results ◽  
Scale Data

This paper aims to evaluate the reliability, validity and unidimentionality of the scales. A prepilot study was completed that involved three manufacturing managers and four academic experts to improve content validity. A pilot study was then completed that 108 middle and senior manufacturing managers in HCM City and neighboring provinces. Cronbach’s alpha and EFA analysis was used to assess the reliability validity and unidimentionality of the scales. The initial results show that mass customization capability, product modularity, supplier integration may have reliability, validity and unidimentionality; customer integration may not have unidimentionality. Therefore, large-scale data collection and analysis will have collected and analyzed to validate the instruments, test the model and research hypotheses.

scholarly journals Large-scale Data Collection and Analysis via a Gamified Intelligent Crowdsourcing Platform

2019 ◽  
Vol 16 (4) ◽  
pp. 427-436 ◽  
Author(s):  
Simone Hantke ◽  
Tobias Olenyi ◽  
Christoph Hausner ◽  
Tobias Appel ◽  
Björn Schuller
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Large Scale Data ◽  
Data Collection And Analysis ◽  
Scale Data

scholarly journals Support Vector Machines in Big Data Classification: A Systematic Literature Review

2021 ◽  
Author(s):  
Mohammad Hassan Almaspoor ◽  
Ali Safaei ◽  
Afshin Salajegheh ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Support Vector ◽  
Research Areas ◽  
Large Scale Data ◽  
Training Samples ◽  
Big Data Classification ◽  
Scale Data

Abstract Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

scholarly journals Data Lake Ecosystem Workflow

2021 ◽  
Author(s):  
R. Salter ◽  
Quyen Dong ◽  
Cody Coleman ◽  
Maria Seale ◽  
Alicia Ruvinsky ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Lake Ecosystem ◽  
Data Governance ◽  
Government Organizations ◽  
Large Scale Data ◽  
Scale Data

The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

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.

New Frontiers for E-Learning in Education

2018 ◽  
pp. 220-240
Author(s):  
Mohammad Zubair Khan ◽  
Yasser M. Alginahi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Repositories ◽  
Useful Knowledge ◽  
Leading Role ◽  
Large Scale Data ◽  
E Learning ◽  
Base Management ◽  
Wide Group ◽  
Scale Data

Big Data research is playing a leading role in investigating a wide group of issues fundamentally emerging concerning Database, Data Warehousing, and Data Mining research. Analytics research is intended to develop complex procedures running over large-scale data repositories with the objective of extracting useful knowledge hidden in such repositories. A standout amongst the most noteworthy application situations where Big Data emerge is, without uncertainty, logical figuring. Here, researchers and analysts create immense measures of information everyday by means of investigations (e.g., disciplines like high vitality material science, space science, bioinformatics, etc.). Nevertheless, separating helpful learning for basic leadership purposes from these enormous, vast scale data repositories are practically inconceivable for genuine Data Base Management Systems (DBMS), is inspired investigation tools.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

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.

Sign in / Sign up

Export Citation Format

Share Document