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 digitaltw∈′,enab≤s∫eractionsbetweenaphysicalsystemanditscomputationalmodelrepresentation.Withthegoalof⊃port∈goperationaldecisions,anS3usesreal-timedataomthephysicalsystem,whichisgatheredviasensors.Thisreal-timedataisalsosaved∈anenterprisedatas→ra≥system(EDSS),soitcanbeusedashis→ricaldataforfutureuse.Bothreal-timeandhis→ricaldataarethenusedas∈puts→thed⇔erentcompo≠ntsofanS3,whichtyπcallycomprisesseveralmode̲s:dataacquisition,siμlation,optimisation,maχ≠≤arn∈g,andanactuator'. 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
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), .
(doi:10.1080/17517575.2020.1777587).
Abstract
A symbiotic simulation system (S3), sometimes also called a digitaltw∈′,enab≤s∫eractionsbetweenaphysicalsystemanditscomputationalmodelrepresentation.Withthegoalof⊃port∈goperationaldecisions,anS3usesreal-timedataomthephysicalsystem,whichisgatheredviasensors.Thisreal-timedataisalsosaved∈anenterprisedatas→ra≥system(EDSS),soitcanbeusedashis→ricaldataforfutureuse.Bothreal-timeandhis→ricaldataarethenusedas∈puts→thed⇔erentcompo≠ntsofanS3,whichtyπcallycomprisesseveralmode̲s:dataacquisition,siμlation,optimisation,maχ≠≤arn∈g,andanactuator'. 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.
Text
TEIS-2020-0090.R1_Proof_hi
- Accepted Manuscript
More information
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
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Date deposited: 08 Jun 2020 16:32
Last modified: 17 Mar 2024 05:37
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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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