6533b835fe1ef96bd129e979

RESEARCH PRODUCT

Issues in synthetic data generation for advanced manufacturing

Don LibesSanjay JainDavid Lechevalier

subject

Test data generationComputer science05 social sciences0402 animal and dairy science04 agricultural and veterinary sciences040201 dairy & animal scienceData scienceSynthetic dataData modelingLead (geology)0502 economics and businessData analysisSynthetic data generationAdvanced manufacturing050203 business & management

description

To have any chance of application in real world, advanced manufacturing research in data analytics needs to explore and prove itself with real-world manufacturing data. Limited access to real-world data largely contrasts with the need for data of varied types and larger quantity for research. Use of virtual data is a promising approach to make up for the lack of access. This paper explores the issues, identifies challenges, and suggests requirements and desirable features in the generation of virtual data. These issues, requirements, and features can be used by researchers to build virtual data generators and gain experience that will provide data to data scientists while avoiding known or potential problems. This, in turn, will lead to better requirements and features in future virtual data generators.

yearjournalcountryeditionlanguage
2017-12-012017 IEEE International Conference on Big Data (Big Data)
https://doi.org/10.1109/bigdata.2017.8258117