Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing

Pravallika Devineni; Panchapakesan Ganesh; Nikhil Sivadas; Abhijeet Dhakane; Ketan Maheshwari; Drahomira Herrmannova; Ramakrishnan Kannan; Seung Hwan Lim; Thomas E. Potok; Jordan Chipka; Priyantha Mudalige; Mark Coletti; Sajal Dash; Arnab K. Paul; Sarp Oral; Feiyi Wang; Bill Kay; Melissa Allen-Dumas; Christa Brelsford; Joshua New; Andy Berres; Kuldeep Kurte; Jibonananda Sanyal; Levi Sweet; Chathika Gunaratne; Maxim Ziatdinov; Rama Vasudevan; Sergei Kalinin; Olivera Kotevska; Jean Bilheux; Hassina Bilheux; Garrett E. Granroth; Thomas Proffen; Rick Riedel; Peter Peterson; Shruti Kulkarni; Kyle Kelley; Stephen Jesse; Maryam Parsa

doi:10.1007/978-3-030-96498-6_21

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing

Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan, Seung Hwan Lim, Thomas E. Potok, Jordan Chipka, Priyantha Mudalige, Mark Coletti, Sajal Dash, Arnab K. Paul, Sarp Oral, Feiyi Wang, Bill Kay, Melissa Allen-Dumas, Christa Brelsford, Joshua NewAndy Berres, Kuldeep Kurte, Jibonananda Sanyal, Levi Sweet, Chathika Gunaratne, Maxim Ziatdinov, Rama Vasudevan, Sergei Kalinin, Olivera Kotevska, Jean Bilheux, Hassina Bilheux, Garrett E. Granroth, Thomas Proffen, Rick Riedel, Peter Peterson, Shruti Kulkarni, Kyle Kelley, Stephen Jesse, Maryam Parsa

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

Abstract

The 2021 Smoky Mountains Computational Sciences and Engineering Conference enlists scientists from across Oak Ridge National Laboratory (ORNL) and industry to be data sponsors and help create data analytics and edge computing challenges for eminent datasets in a variety of scientific domains. This work describes the significance of each of the eight datasets and their associated challenge questions. The challenge questions for each dataset were required to cover multiple difficulty levels. An international call for participation was sent to students, asking them to form teams of up to six people and apply novel data analytics and edge computing methods to solve these challenges.

Original language	English
Title of host publication	Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers
Editors	[given-name]Jeffrey Nichols, [given-name]Arthur ‘Barney’ Maccabe, James Nutaro, Swaroop Pophale, Pravallika Devineni, Theresa Ahearn, Becky Verastegui
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	361-382
Number of pages	22
ISBN (Print)	9783030964979
DOIs	https://doi.org/10.1007/978-3-030-96498-6_21
State	Published - 2022
Event	21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021 - Virtual, Online Duration: Oct 18 2021 → Oct 20 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1512 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021
City	Virtual, Online
Period	10/18/21 → 10/20/21

Funding

Dataset generation for Challenge 1 was supported by the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. Through the ASCR Leadership Computing Challenge (ALCC) program, 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. Dataset generation for Challenge 2 was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under contract number DE-AC05-00OR22725. Dataset generation for Challenge 3 used resources from General Motors. Dataset generation for Challenge 4 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. Dataset generation for Challenge 5 was completed by researchers at Oak Ridge National Laboratory sponsored by the DOE Office of Science as a part of the research in Multi-Sector Dynamics within the Earth and Environmental System Modeling Program as part of the Integrated Multiscale Multisector Modeling (IM3) Scientific Focus Area led by Pacific Northwest National Laboratory. The dataset for Challenge 7 was acquired at the Spallation Neutron Source which is sponsored by the User Facilities Division of the Department of Energy. The research for generating datasets for challenges 6 and 8 was conducted at and partially supported by the at the Center for Nanophase Materials Sciences, a US DOE Office of Science User Facility. Acknowledgment. Dataset generation for Challenge 1 was supported by the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. Through the ASCR Leadership Computing Challenge (ALCC) program, 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. Dataset generation for Challenge 2 was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under contract number DE-AC05-00OR22725. Dataset generation for Challenge 3 used resources from General Motors.

Keywords

Data analytics
Edge computing
Machine learning
Scientific data

Access to Document

10.1007/978-3-030-96498-6_21

Cite this

Devineni, P., Ganesh, P., Sivadas, N., Dhakane, A., Maheshwari, K., Herrmannova, D., Kannan, R., Lim, S. H., Potok, T. E., Chipka, J., Mudalige, P., Coletti, M., Dash, S., Paul, A. K., Oral, S., Wang, F., Kay, B., Allen-Dumas, M., Brelsford, C., ... Parsa, M. (2022). Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. In J. Nichols, A. B. Maccabe, J. Nutaro, S. Pophale, P. Devineni, T. Ahearn, & B. Verastegui (Eds.), Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers (pp. 361-382). (Communications in Computer and Information Science; Vol. 1512 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-96498-6_21

Devineni, Pravallika ; Ganesh, Panchapakesan ; Sivadas, Nikhil et al. / Smoky Mountain Data Challenge 2021 : An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. editor / [given-name]Jeffrey Nichols ; [given-name]Arthur ‘Barney’ Maccabe ; James Nutaro ; Swaroop Pophale ; Pravallika Devineni ; Theresa Ahearn ; Becky Verastegui. Springer Science and Business Media Deutschland GmbH, 2022. pp. 361-382 (Communications in Computer and Information Science).

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abstract = "The 2021 Smoky Mountains Computational Sciences and Engineering Conference enlists scientists from across Oak Ridge National Laboratory (ORNL) and industry to be data sponsors and help create data analytics and edge computing challenges for eminent datasets in a variety of scientific domains. This work describes the significance of each of the eight datasets and their associated challenge questions. The challenge questions for each dataset were required to cover multiple difficulty levels. An international call for participation was sent to students, asking them to form teams of up to six people and apply novel data analytics and edge computing methods to solve these challenges.",

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author = "Pravallika Devineni and Panchapakesan Ganesh and Nikhil Sivadas and Abhijeet Dhakane and Ketan Maheshwari and Drahomira Herrmannova and Ramakrishnan Kannan and Lim, \{Seung Hwan\} and Potok, \{Thomas E.\} and Jordan Chipka and Priyantha Mudalige and Mark Coletti and Sajal Dash and Paul, \{Arnab K.\} and Sarp Oral and Feiyi Wang and Bill Kay and Melissa Allen-Dumas and Christa Brelsford and Joshua New and Andy Berres and Kuldeep Kurte and Jibonananda Sanyal and Levi Sweet and Chathika Gunaratne and Maxim Ziatdinov and Rama Vasudevan and Sergei Kalinin and Olivera Kotevska and Jean Bilheux and Hassina Bilheux and Granroth, \{Garrett E.\} and Thomas Proffen and Rick Riedel and Peter Peterson and Shruti Kulkarni and Kyle Kelley and Stephen Jesse and Maryam Parsa",

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year = "2022",

doi = "10.1007/978-3-030-96498-6\_21",

language = "English",

isbn = "9783030964979",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "361--382",

editor = "[given-name]Jeffrey Nichols and Maccabe, \{[given-name]Arthur {\textquoteleft}Barney{\textquoteright}\} and James Nutaro and Swaroop Pophale and Pravallika Devineni and Theresa Ahearn and Becky Verastegui",

booktitle = "Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers",

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Devineni, P, Ganesh, P, Sivadas, N, Dhakane, A, Maheshwari, K, Herrmannova, D, Kannan, R , Lim, SH , Potok, TE, Chipka, J, Mudalige, P, Coletti, M, Dash, S, Paul, AK, Oral, S , Wang, F, Kay, B, Allen-Dumas, M, Brelsford, C, New, J, Berres, A, Kurte, K, Sanyal, J, Sweet, L, Gunaratne, C, Ziatdinov, M, Vasudevan, R, Kalinin, S, Kotevska, O , Bilheux, J , Bilheux, H , Granroth, GE , Proffen, T, Riedel, R, Peterson, P , Kulkarni, S , Kelley, K , Jesse, S & Parsa, M 2022, Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. in J Nichols, AB Maccabe, J Nutaro, S Pophale, P Devineni, T Ahearn & B Verastegui (eds), Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. Communications in Computer and Information Science, vol. 1512 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 361-382, 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, Virtual, Online, 10/18/21. https://doi.org/10.1007/978-3-030-96498-6_21

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. / Devineni, Pravallika; Ganesh, Panchapakesan; Sivadas, Nikhil et al.
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. ed. / [given-name]Jeffrey Nichols; [given-name]Arthur ‘Barney’ Maccabe; James Nutaro; Swaroop Pophale; Pravallika Devineni; Theresa Ahearn; Becky Verastegui. Springer Science and Business Media Deutschland GmbH, 2022. p. 361-382 (Communications in Computer and Information Science; Vol. 1512 CCIS).

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

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AB - The 2021 Smoky Mountains Computational Sciences and Engineering Conference enlists scientists from across Oak Ridge National Laboratory (ORNL) and industry to be data sponsors and help create data analytics and edge computing challenges for eminent datasets in a variety of scientific domains. This work describes the significance of each of the eight datasets and their associated challenge questions. The challenge questions for each dataset were required to cover multiple difficulty levels. An international call for participation was sent to students, asking them to form teams of up to six people and apply novel data analytics and edge computing methods to solve these challenges.

KW - Data analytics

KW - Edge computing

KW - Machine learning

KW - Scientific data

UR - https://www.scopus.com/pages/publications/85127053382

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DO - 10.1007/978-3-030-96498-6_21

M3 - Conference contribution

AN - SCOPUS:85127053382

SN - 9783030964979

T3 - Communications in Computer and Information Science

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BT - Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers

A2 - Nichols, [given-name]Jeffrey

A2 - Maccabe, [given-name]Arthur ‘Barney’

A2 - Nutaro, James

A2 - Pophale, Swaroop

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PB - Springer Science and Business Media Deutschland GmbH

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Devineni P, Ganesh P, Sivadas N, Dhakane A, Maheshwari K, Herrmannova D et al. Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. In Nichols J, Maccabe AB, Nutaro J, Pophale S, Devineni P, Ahearn T, Verastegui B, editors, Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation - 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 361-382. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-96498-6_21

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing

Abstract

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