Zobrazeno 1 - 10
of 29
pro vyhledávání: '"VAN DAM, KERSTIN KLEESE"'
Autor:
Ha, Sungsoo, Jeong, Wonyong, Matyasfalvi, Gyorgy, Xie, Cong, Huck, Kevin, Choi, Jong Youl, Malik, Abid, Tang, Li, Van Dam, Hubertus, Pouchard, Line, Xu, Wei, Yoo, Shinjae, D'Imperio, Nicholas, Van Dam, Kerstin Kleese
Because of the limits input/output systems currently impose on high-performance computing systems, a new generation of workflows that include online data reduction and analysis is emerging. Diagnosing their performance requires sophisticated performa
Externí odkaz:
http://arxiv.org/abs/2008.13742
Autor:
Malitsky, Nikolay, Chaudhary, Aashish, Jourdain, Sebastien, Cowan, Matt, O'Leary, Patrick, Hanwell, Marcus, Van Dam, Kerstin Kleese
Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three Vs (Volume, Velocity, and Variety) of experimental data and the scale of c
Externí odkaz:
http://arxiv.org/abs/1805.04886
Autor:
Pouchard, Line, Baldwin, Sterling, Elsethagen, Todd, Gamboa, Carlos, Jha, Shantenu, Raju, Bibi, Stephan, Eric, Tang, Li, Van Dam, Kerstin Kleese
We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: sc
Externí odkaz:
http://arxiv.org/abs/1805.00967
Autor:
Akopov, Z., Amerio, Silvia, Asner, David, Avetisyan, Eduard, Barring, Olof, Beacham, James, Bellis, Matthew, Bernardi, Gregorio, Bethke, Siegfried, Boehnlein, Amber, Brooks, Travis, Browder, Thomas, Brun, Rene, Cartaro, Concetta, Cattaneo, Marco, Chen, Gang, Corney, David, Cranmer, Kyle, Culbertson, Ray, Dallmeier-Tiessen, Sunje, Denisov, Dmitri, Diaconu, Cristinel, Dodonov, Vitaliy, Doyle, Tony, Dubois-Felsmann, Gregory, Ernst, Michael, Gasthuber, Martin, Geiser, Achim, Gianotti, Fabiola, Giubellino, Paolo, Golutvin, Andrey, Gordon, John, Guelzow, Volker, Hara, Takanori, Hayashii, Hisaki, Heiss, Andreas, Hemmer, Frederic, Hernandez, Fabio, Heyes, Graham, Holzner, Andre, Igo-Kemenes, Peter, Iijima, Toru, Incandela, Joe, Jones, Roger, Kemp, Yves, van Dam, Kerstin Kleese, Knobloch, Juergen, Kreincik, David, Lassila-Perini, Kati, Diberder, Francois Le, Levonian, Sergey, Levy, Aharon, Li, Qizhong, Lobodzinski, Bogdan, Maggi, Marcello, Malka, Janusz, Mele, Salvatore, Mount, Richard, Neal, Homer, Olsson, Jan, Ozerov, Dmitri, Piilonen, Leo, Punzi, Giovanni, Regimbal, Kevin, Riley, Daniel, Roney, Michael, Roser, Robert, Ruf, Thomas, Sakai, Yoshihide, Sasaki, Takashi, Schnell, Gunar, Schroeder, Matthias, Schutz, Yves, Shiers, Jamie, Smith, Tim, Snider, Rick, South, David M., Denis, Rick St., Steder, Michael, Van Wezel, Jos, Varnes, Erich, Votava, Margaret, Wang, Yifang, Weygand, Dennis, White, Vicky, Wichmann, Katarzyna, Wolbers, Stephen, Yamauchi, Masanori, Yavin, Itay, von der Schmitt, Hans
Data from high-energy physics (HEP) experiments are collected with significant financial and human effort and are mostly unique. An inter-experimental study group on HEP data preservation and long-term analysis was convened as a panel of the Internat
Externí odkaz:
http://arxiv.org/abs/1205.4667
Autor:
Zhong, Wen, Xu, Wei, Yager, Kevin G., Doerk, Gregory S., Zhao, Jian, Tian, Yunke, Ha, Sungsoo, Xie, Cong, Zhong, Yuan, Mueller, Klaus, Van Dam, Kerstin Kleese
Publikováno v:
In Visual Informatics March 2018 2(1):14-25
Akademický článek
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Autor:
Kalinin, Sergei V, Foster, Ian, Weber, Dieter, Clausen, Alexander, Dunin-Borkowski, Rafal E., Kalidindi, Surya, Lookman, Turab, van Dam, Kerstin Kleese, Yager, Kevin G, Campbell, Stuart I, Farnsworth, Richard, van Dam, Maartje
Publikováno v:
2020 Springer Nature Switzerland AG. : World Scientific 1-34 (2020). doi:10.1142/9789811204579_0005
Handbook on Big Data and Machine Learning in the Physical Sciences: (In 2 Volumes)Volume 1: Big Data Methods in Experimental Materials DiscoveryVolume 2: Advanced Analysis Solutions for Leading Experimental Techniques / Kalinin, Sergei V {Oak Ridge National LaboratoryUSA} ; : World Scientific, 2020, ; ISBN: 978-981-12-0444-9=978-981-12-0453-1 ; doi:10.1142/11389-vol2
Handbook on Big Data and Machine Learning in the Physical Sciences: (In 2 Volumes)Volume 1: Big Data Methods in Experimental Materials DiscoveryVolume 2: Advanced Analysis Solutions for Leading Experimental Techniques / Kalinin, Sergei V {Oak Ridge National LaboratoryUSA} ; : World Scientific, 2020, ; ISBN: 978-981-12-0444-9=978-981-12-0453-1 ; doi:10.1142/11389-vol2
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3364::9622dd078f064ffa9318ca2123bc102d
https://hdl.handle.net/2128/25432
https://hdl.handle.net/2128/25432
Autor:
Kalinin, Sergei V, Foster, Ian, Kalidindi, Surya, Lookman, Turab, van Dam, Kerstin Kleese, Yager, Kevin G, Campbell, Stuart I, Farnsworth, Richard, van Dam, Maartje, Weber, Dieter, Clausen, Alexander, Dunin-Borkowski, Rafal E.
Publikováno v:
Handbook on Big Data and Machine Learning in the Physical Sciences-Volume 2: Advanced Analysis Solutions for Leading Experimental Techniques
Akademický článek
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Akademický článek
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