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103 results on '"Panácek A"'

17. ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

Author

Antcheva, Ilka, Ballintijn, Maarten, Bellenot, Bertrand, Biskup, Marek, Brun, Rene, Buncic, Nenad, Canal, Philippe, Casadei, Diego, Couet, Olivier, Fine, Valery, Franco, Leandro, Ganis, Gerardo, Gheata, Andrei, Maline, David Gonzalez, Goto, Masaharu, Iwaszkiewicz, Jan, Kreshuk, Anna, Segura, Diego Marcos, Maunder, Richard, Moneta, Lorenzo, Naumann, Axel, Offermann, Eddy, Onuchin, Valeriy, Panacek, Suzanne, Rademakers, Fons, Russo, Paul, and Tadel, Matevz

Subjects
Physics - Data Analysis, Statistics and Probability, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way.

Published
2015
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20. Sepsis-Associated Acute Kidney Disease

Author

Angus, Derek C., Chawla, Lakhmir S., Huang, David T., Keener, Christopher, Kellum, John A., Lucko, Nicole, Palevsky, Paul M., Pike, Francis, Singbartl, Kai, Smith, Ali, Yealy, Donald M., Yende, Sachin, Barnato, Amber E., Eaton, Tammy L., Gimbel, Elizabeth, Landis, Kyle, Stapleton, Diana K., Weissfeld, Lisa A., Willochell, Michael, Wofford, Kourtney A., Kulstad, Erik, Watts, Hannah, Venkatv, Arvind, Hou, Peter C., Massaro, Anthony, Parmar, Siddharth, Limkakeng, Alexander T., Jr., Brewer, Kori, Delbridge, Theodore R., Mainhart, Allison, Miner, James R., Allen, Todd L., Grissom, Colin K., Swadron, Stuart, Conrad, Steven A., Carlson, Richard, LoVecchio, Frank, Bajwa, Ednan K., Filbin, Michael R., Parry, Blair A., Ellender, Timothy J., Sama, Andrew E., Fine, Jonathan, Nafeei, Soheil, Terndrup, Thomas, Wojnar, Margaret, Pearl, Ronald G., Wilber, Scott T., Sinert, Richard, Orban, David J., Wilson, Jason W., Ufberg, Jacob W., Albertson, Timothy, Panacek, Edward A., Parekh, Sohan, Gunn, Scott R., Rittenberger, Jon S., Wadas, Richard J., Edwards, Andrew R., Kelly, Matthew, Wang, Henry E., Holmes, Talmage M., McCurdy, Michael T., Weinert, Craig, Harris, Estelle S., Self, Wesley H., Dubinski, Diane, Phillips, Carolyn A., Migues, Ronald M., Peerapornratana, Sadudee, Priyanka, Priyanka, and Wang, Shu

Published
2020
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