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Combining symbiotic simulation systems with enterprise data storage systems for real-time decision making

Combining symbiotic simulation systems with enterprise data storage systems for real-time decision making
Combining symbiotic simulation systems with enterprise data storage systems for real-time decision making
A symbiotic simulation system (S3), sometimes also called a `digital twin', enables interactions between a physical system and its computational model representation. With the goal of supporting operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors. This real-time data is also saved in an enterprise data storage system (EDSS), so it can be used as historical data for future use. Both real-time and historical data are then used as inputs to the different components of an S3, which typically comprises several modules: data acquisition, simulation, optimisation, machine learning, and an `actuator'. The latter is needed when there is not a human agent between the S3 and the system. Given the amount of data generated by today's smart systems, an S3 needs to be coupled with an EDSS. Furthermore, the S3 may produce a large amount of output data that needs to be stored, since it might be re-used by the machine learning module to make the S3 adaptive in dynamic scenarios. With the goal of supporting real-time operational decision-making -- specially in Industry 4.0 applications such as smart cities, smart factories, intelligent transportation systems, and digital supply chains --, this paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. Moreover, the paper reviews the state-of-the-art in S3, and analyses how these systems can interact with EDSSs to make real-time decision making a reality. Finally, the paper also points out several research challenges in S3.
Symbiotic simulation systems, big data, digital twin, enterprise data storage systems, online simulation, real-time decision
1751-7575
230-247
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Corlu, Canan Gunes
ecb0f999-21d4-41e2-8cab-58a33706f09e
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
Monks, Thomas
ccaa21dc-b1fd-4b5a-b114-8c36f3107d40
de la Torre, Rocio
414ea334-70cd-4299-b4c8-836a3fc77439
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Corlu, Canan Gunes
ecb0f999-21d4-41e2-8cab-58a33706f09e
Juan, Angel A.
a08d6aac-1e9b-4537-81a7-29a1ba791f26
Monks, Thomas
ccaa21dc-b1fd-4b5a-b114-8c36f3107d40
de la Torre, Rocio
414ea334-70cd-4299-b4c8-836a3fc77439

Onggo, Bhakti Stephan, Corlu, Canan Gunes, Juan, Angel A., Monks, Thomas and de la Torre, Rocio (2020) Combining symbiotic simulation systems with enterprise data storage systems for real-time decision making. Enterprise Information Systems, 15 (2), 230-247. (doi:10.1080/17517575.2020.1777587).

Record type: Article

Abstract

A symbiotic simulation system (S3), sometimes also called a `digital twin', enables interactions between a physical system and its computational model representation. With the goal of supporting operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors. This real-time data is also saved in an enterprise data storage system (EDSS), so it can be used as historical data for future use. Both real-time and historical data are then used as inputs to the different components of an S3, which typically comprises several modules: data acquisition, simulation, optimisation, machine learning, and an `actuator'. The latter is needed when there is not a human agent between the S3 and the system. Given the amount of data generated by today's smart systems, an S3 needs to be coupled with an EDSS. Furthermore, the S3 may produce a large amount of output data that needs to be stored, since it might be re-used by the machine learning module to make the S3 adaptive in dynamic scenarios. With the goal of supporting real-time operational decision-making -- specially in Industry 4.0 applications such as smart cities, smart factories, intelligent transportation systems, and digital supply chains --, this paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. Moreover, the paper reviews the state-of-the-art in S3, and analyses how these systems can interact with EDSSs to make real-time decision making a reality. Finally, the paper also points out several research challenges in S3.

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TEIS-2020-0090.R1_Proof_hi - Accepted Manuscript
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Accepted/In Press date: 31 May 2020
e-pub ahead of print date: 18 June 2020
Keywords: Symbiotic simulation systems, big data, digital twin, enterprise data storage systems, online simulation, real-time decision

Identifiers

Local EPrints ID: 441291
URI: http://eprints.soton.ac.uk/id/eprint/441291
ISSN: 1751-7575
PURE UUID: f5da9be5-97df-469e-a65f-3c61444028a2
ORCID for Bhakti Stephan Onggo: ORCID iD orcid.org/0000-0001-5899-304X

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Date deposited: 08 Jun 2020 16:32
Last modified: 09 Jan 2022 05:47

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Contributors

Author: Canan Gunes Corlu
Author: Angel A. Juan
Author: Thomas Monks
Author: Rocio de la Torre

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