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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - From Big Data to Massive Data
T2 - Towards a Massive Data Storage Solution for the Internet of Senses
AU - Fayoumi, Amjad
AU - Orachorn, Chanapat
AU - Shi, Xiao
PY - 2024/10/1
Y1 - 2024/10/1
N2 - The increased maturity of current digitalization techniques expands the scope of practical deployments in various fields, including the precipitation of “digital biology” as a novel and highly important new field of research. This research area specializes in the convergence of various digital technologies and investigations into biology, offering the possibility to produce new comprehension of and applications for living systems by leveraging frameworks of digital technology. The enhanced scope of current capacities in computation facilitates simulated models of multifaceted biological artefacts and novel ways to gather, analyze, and interpret data, moving far beyond the primitive scope of legacy solutions in this field. This paper explores potential “massive data” databases, developing a conceptual model to move beyond the existing “big data” paradigm.
AB - The increased maturity of current digitalization techniques expands the scope of practical deployments in various fields, including the precipitation of “digital biology” as a novel and highly important new field of research. This research area specializes in the convergence of various digital technologies and investigations into biology, offering the possibility to produce new comprehension of and applications for living systems by leveraging frameworks of digital technology. The enhanced scope of current capacities in computation facilitates simulated models of multifaceted biological artefacts and novel ways to gather, analyze, and interpret data, moving far beyond the primitive scope of legacy solutions in this field. This paper explores potential “massive data” databases, developing a conceptual model to move beyond the existing “big data” paradigm.
U2 - 10.1109/BDAI62182.2024.10692750
DO - 10.1109/BDAI62182.2024.10692750
M3 - Conference contribution/Paper
SN - 9798350352016
BT - The 7th International Conference on Big Data and Artificial Intelligence
PB - IEEE
ER -