An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation

Zhuo Yao; Dali Wang; Yifan Wang; Fengming Yuan

doi:10.1007/978-3-319-93701-4_4

An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation

Zhuo Yao, Dali Wang, Yifan Wang, Fengming Yuan

Earth Systems Modeling

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Based on previous work on in-situ data transfer infrastructure and compiler-based software analysis, we have designed a virtual observation system for real time computer simulations. This paper presents an event detection framework for a virtual observation system. By using signal processing and detection approaches to the memory-based data streams, this framework can be reconfigured to capture high-frequency events and low-frequency events. These approaches used in the framework can dramatically reduce the data transfer needed for in-situ data analysis (between distributed computing nodes or between the CPU/GPU nodes). In the paper, we also use a terrestrial ecosystem system simulation within the Earth System Model to demonstrate the practical values of this effort.

Original language	English
Title of host publication	Computational Science – ICCS 2018 - 18th International Conference, Proceedings
Editors	Valeria V. Krzhizhanovskaya, Michael Harold Lees, Peter M. Sloot, Jack Dongarra, Yong Shi, Yingjie Tian, Haohuan Fu
Publisher	Springer Verlag
Pages	44-55
Number of pages	12
ISBN (Print)	9783319937007
DOIs	https://doi.org/10.1007/978-3-319-93701-4_4
State	Published - 2018
Event	18th International Conference on Computational Science, ICCS 2018 - Wuxi, China Duration: Jun 11 2018 → Jun 13 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10861 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Computational Science, ICCS 2018
Country/Territory	China
City	Wuxi
Period	06/11/18 → 06/13/18

Funding

Acknowledgements. This research was funded by the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER) program, and Advanced Scientific Computing Research (ASCR) program, and LDRD #8389. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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10.1007/978-3-319-93701-4_4

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Yao, Z., Wang, D., Wang, Y., & Yuan, F. (2018). An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. Sloot, J. Dongarra, Y. Shi, Y. Tian, & H. Fu (Eds.), Computational Science – ICCS 2018 - 18th International Conference, Proceedings (pp. 44-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10861 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93701-4_4

Yao, Zhuo ; Wang, Dali ; Wang, Yifan et al. / An Event Detection Framework for Virtual Observation System : Anomaly Identification for an ACME Land Simulation. Computational Science – ICCS 2018 - 18th International Conference, Proceedings. editor / Valeria V. Krzhizhanovskaya ; Michael Harold Lees ; Peter M. Sloot ; Jack Dongarra ; Yong Shi ; Yingjie Tian ; Haohuan Fu. Springer Verlag, 2018. pp. 44-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Based on previous work on in-situ data transfer infrastructure and compiler-based software analysis, we have designed a virtual observation system for real time computer simulations. This paper presents an event detection framework for a virtual observation system. By using signal processing and detection approaches to the memory-based data streams, this framework can be reconfigured to capture high-frequency events and low-frequency events. These approaches used in the framework can dramatically reduce the data transfer needed for in-situ data analysis (between distributed computing nodes or between the CPU/GPU nodes). In the paper, we also use a terrestrial ecosystem system simulation within the Earth System Model to demonstrate the practical values of this effort.",

author = "Zhuo Yao and Dali Wang and Yifan Wang and Fengming Yuan",

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Yao, Z, Wang, D, Wang, Y & Yuan, F 2018, An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation. in VV Krzhizhanovskaya, MH Lees, PM Sloot, J Dongarra, Y Shi, Y Tian & H Fu (eds), Computational Science – ICCS 2018 - 18th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10861 LNCS, Springer Verlag, pp. 44-55, 18th International Conference on Computational Science, ICCS 2018, Wuxi, China, 06/11/18. https://doi.org/10.1007/978-3-319-93701-4_4

An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation. / Yao, Zhuo; Wang, Dali; Wang, Yifan et al.
Computational Science – ICCS 2018 - 18th International Conference, Proceedings. ed. / Valeria V. Krzhizhanovskaya; Michael Harold Lees; Peter M. Sloot; Jack Dongarra; Yong Shi; Yingjie Tian; Haohuan Fu. Springer Verlag, 2018. p. 44-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10861 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - An Event Detection Framework for Virtual Observation System

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AU - Yao, Zhuo

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AB - Based on previous work on in-situ data transfer infrastructure and compiler-based software analysis, we have designed a virtual observation system for real time computer simulations. This paper presents an event detection framework for a virtual observation system. By using signal processing and detection approaches to the memory-based data streams, this framework can be reconfigured to capture high-frequency events and low-frequency events. These approaches used in the framework can dramatically reduce the data transfer needed for in-situ data analysis (between distributed computing nodes or between the CPU/GPU nodes). In the paper, we also use a terrestrial ecosystem system simulation within the Earth System Model to demonstrate the practical values of this effort.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Yao Z, Wang D, Wang Y, Yuan F. An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation. In Krzhizhanovskaya VV, Lees MH, Sloot PM, Dongarra J, Shi Y, Tian Y, Fu H, editors, Computational Science – ICCS 2018 - 18th International Conference, Proceedings. Springer Verlag. 2018. p. 44-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93701-4_4

An Event Detection Framework for Virtual Observation System: Anomaly Identification for an ACME Land Simulation

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