The Data Deconflation Problem: Moving from Classical to Emerging Solutions

Roger Hallman, Roger Hallman, George Cybenko

Abstract

Data conflation refers to the superposition data produced by diverse processes resulting in complex, combined data objects. We define the data deconflation problem as the challenge of identifying and separating these complex data objects into their individual, constituent objects. Solutions to classical deconflation problems (e.g., the Cocktail Party Problem) use established linear algebra techniques, but it is not clear that those solutions are extendable to broader classes of conflated data objects. This paper surveys both classical and emerging data deconflation problems, as well as presenting an approach towards a general solution utilizing deep reinforcement learning and generative adversarial networks.

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Paper Citation


in Harvard Style

Hallman R. and Cybenko G. (2021). The Data Deconflation Problem: Moving from Classical to Emerging Solutions. In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs, ISBN 978-989-758-504-3, pages 375-380. DOI: 10.5220/0010530403750380


in Bibtex Style

@conference{ai4eiots21,
author={Roger Hallman and George Cybenko},
title={The Data Deconflation Problem: Moving from Classical to Emerging Solutions},
booktitle={Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs,},
year={2021},
pages={375-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010530403750380},
isbn={978-989-758-504-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs,
TI - The Data Deconflation Problem: Moving from Classical to Emerging Solutions
SN - 978-989-758-504-3
AU - Hallman R.
AU - Cybenko G.
PY - 2021
SP - 375
EP - 380
DO - 10.5220/0010530403750380