scholarly journals Cloud Based Meta Data Driven Product Model

2016 ◽  
pp. 356-364
Author(s):  
Arun Kumar Singh ◽  
B. Gurumoorthy ◽  
Latha Christie
Keyword(s):  
Data Driven ◽  
Product Model ◽  
Meta Data

Development of a Pilot Manufacturing Cyberinfrastructure With an Information Rich Mechanical CAD 3D Model Repository

2019 ◽  
Author(s):  
Akshay Bharadwaj ◽  
Yang Xu ◽  
Atin Angrish ◽  
Yong Chen ◽  
Binil Starly
Keyword(s):  
Product Design ◽  
Academic Research ◽  
Digital Design ◽  
Data Driven ◽  
Concept Design ◽  
Design Data ◽  
Meta Data ◽  
Pilot Implementation ◽  
Project Data ◽  
Manufacturing Data

Abstract Data driven advanced manufacturing research is dependent on access to large datasets made available from across the product lifecycle — from the concept design phase all the way down to end use and disposal. Despite such data being generated at a rapid pace, most product design data is archived in inaccessible silos. This is particularly acute in academic research laboratories and with data generated during product design and manufacturing courses. This project seeks to create an infrastructure that allow users (academia and the general public) to easily upload project data and related meta-data. Current manufacturing research must shift from siloed repositories of product manufacturing data to a federated, decentralized, open and inter-operable approach. In this regard, we build ‘FabWave’ a cyber-infrastructure tool designed to capture manufacturing data. In its first pilot implementation, we focused our attention to gathering information rich 3D Mechanical CAD data and related meta-data associated with them, with the intent to make it easier for users to upload and access product design data. We describe workflows that we have initially tested out within the two academic universities and under two different course structures. We have also developed automated workflows to gather license appropriate CAD assemblies from commercial repositories. Our intent is to create the only known largest available CAD model set within academia for enabling research in data-driven computational research in digital design, fabrication and quality control.

Meta-data Driven Data Chunk Based Secure Data Storage for SaaS

2011 ◽  
Vol 5 (1) ◽  
pp. 173-185 ◽  
Author(s):  
Yuliang Shi ◽  
Kun Zhang ◽  
Qingzhong Li
Keyword(s):  
Data Storage ◽  
Data Driven ◽  
Meta Data ◽  
Secure Data

A Meta-Data Driven Tourism Information System Prototype

1997 ◽  
pp. 1-9
Author(s):  
Josef Küng ◽  
Richard Sikora ◽  
Roland R. Wagner ◽  
Wolfram Wöß
Keyword(s):  
Information System ◽  
Data Driven ◽  
Meta Data ◽  
Tourism Information ◽  
System Prototype

scholarly journals An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

2015 ◽  
Vol 6 ◽  
pp. 1978-1999 ◽  
Author(s):  
Richard L Marchese Robinson ◽  
Mark T D Cronin ◽  
Andrea-Nicole Richarz ◽  
Robert Rallo
Keyword(s):  
Data Collection ◽  
Data Driven ◽  
Electronic Format ◽  
Business Rules ◽  
Meta Data ◽  
Future Developments ◽  
Current Article ◽  
Eu Project

Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a “Toy Dataset” presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.

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