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357 results on '"Altintas, Ilkay"'

112. Enabling FAIR research in Earth Science through research objects

Author

Garcia-Silva, Andres, primary, Gomez-Perez, Jose Manuel, additional, Palma, Raul, additional, Krystek, Marcin, additional, Mantovani, Simone, additional, Foglini, Federica, additional, Grande, Valentina, additional, De Leo, Francesco, additional, Salvi, Stefano, additional, Trasatti, Elisa, additional, Romaniello, Vito, additional, Albani, Mirko, additional, Silvagni, Cristiano, additional, Leone, Rosemarie, additional, Marelli, Fulvio, additional, Albani, Sergio, additional, Lazzarini, Michele, additional, Napier, Hazel J., additional, Glaves, Helen M., additional, Aldridge, Timothy, additional, Meertens, Charles, additional, Boler, Fran, additional, Loescher, Henry W., additional, Laney, Christine, additional, Genazzio, Melissa A., additional, Crawl, Daniel, additional, and Altintas, Ilkay, additional

Published
2019
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113. The Evolution of Bits and Bottlenecks in a Scientific Workflow Trying to Keep Up with Technology: Accelerating 4D Image Segmentation Applied to NASA Data

Author

Sellars, Scott L., primary, Graham, John, additional, Mishin, Dmitry, additional, Marcus, Kyle, additional, Altintas, Ilkay, additional, DeFanti, Thomas, additional, Smarr, Larry, additional, Crittenden, Camille, additional, Wuerthwein, Frank, additional, Tatar, Joulien, additional, Nguyen, Phu, additional, Shearer, Eric, additional, Sorooshian, Soroosh, additional, and Ralph, F. Martin, additional

Published
2019
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120. Analytics pipeline for left ventricle segmentation and volume estimation on cardiac MRI using deep learning

Author

Nguyen, Mai H., primary, Altintas, Ilkay, additional, Muse, Evan D., additional, Quer, Giorgio, additional, Steinhubl, Steven, additional, Abdelmaguid, Ehab, additional, Huang, Jolene, additional, Kenchareddy, Sanjay, additional, Singla, Disha, additional, Wilke, Laura, additional, Bobar, Marcus, additional, Carruth, Eric D., additional, and Uys, Dylan, additional

Published
2018
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126. Data Hub Architecture for Smart Cities

Author

Koh, Jason, primary, Sandha, Sandeep, additional, Balaji, Bharathan, additional, Crawl, Daniel, additional, Altintas, Ilkay, additional, Gupta, Rajesh, additional, and Srivastava, Mani, additional

Published
2017
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131. Data through the Computational Lens

Author

Kovalchuk, Sergey V., primary, Abuhay, Tesfamariam M., additional, Altintas, Ilkay, additional, Norman, Michael L., additional, Lees, Michael H., additional, Krzhizhanovskaya, Valeria V., additional, Dongarra, Jack, additional, and Sloot, Peter M.A., additional

Published
2017
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132. The future of scientific workflows

Author

Deelman, Ewa, primary, Peterka, Tom, additional, Altintas, Ilkay, additional, Carothers, Christopher D, additional, van Dam, Kerstin Kleese, additional, Moreland, Kenneth, additional, Parashar, Manish, additional, Ramakrishnan, Lavanya, additional, Taufer, Michela, additional, and Vetter, Jeffrey, additional

Published
2017
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133. Leveraging large sensor streams for robust cloud control

Author

Singh, Alok, primary, Stephan, Eric, additional, Elsethagen, Todd, additional, MacDuff, Matt, additional, Raju, Bibi, additional, Schram, Malachi, additional, van Dam, Kerstin Kleese, additional, Kerbyson, Darren J, additional, and Altintas, Ilkay, additional

Published
2016
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136. Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop

Author

Gollner, Michael, Trouve, Arnaud, Altintas, Ilkay, Block, Jessica, De Callafon, Raymond, Clements, Craig, Cortes, Anna, Ellicott, Evan, Filippi, Jean Baptiste, Finney, Mark, Ide, Kayo, Jenkins, Mary Ann, Jimenez, Daniel, Lautenberger, Christopher, Mandel, Jan, Rochoux, Melanie, and Simeoni, Albert

Subjects
wildfire, data assimilation, fire spread, wildland fire, wui, operational modeling
Abstract

This report presents a record of the discussions that took place during the workshop entitled “Towards Data-Driven Operational Wildfire Spread Modeling” held on January 12-13, 2015, at the University of California, San Diego. The workshop was organized as part of WIFIRE, a collaborative project sponsored by the National Science Foundation (NSF) between San Diego Supercomputer Center, Calit2's Qualcomm Institute and Jacobs School of Engineering at the University of California at San Diego (UCSD) and the Department of Fire Protection Engineering at the University of Maryland (UMD). The objective of WIFIRE is to build a cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior (see http://wifire.ucsd.edu). WIFIRE is funded by NSF Award #1331615 as part of the Interdisciplinary Research in Hazards and Disasters (Hazards SEES) program. The objectives of the WIFIRE workshop were: (1) to identify technical barriers and milestones that need to be overcome in order to develop validated data-driven wildfire spread models and make them operational; and (2) to bring together leading representatives of the wildfire research community, the geosciences community and the fire science community. The wildfire research community has relevant expertise on wildfire operations; the geosciences community has relevant expertise on large-scale effects in wildfires (e.g., the coupling with atmospheric phenomena); the fire science community has relevant expertise on flame-scale effects in wildfires (e.g., the response of the fire to changing local conditions). The workshop was organized around four main topical areas and corresponding breakout groups, including operational rate-of-spread models for wildfire spread, CFD models, wildfire data, and data assimilation (see Appendix A for a description of the WIFIRE workshop program). Our goal in this report is to document and share the substance and scope of the workshop discussions and to thereby invite the wider research community to support, engage in, and contribute to the general effort to develop operational data-driven tools for wildfire spread predictions., The National Science Foundation via Award #1331615 as part of the Interdisciplinary Research in Hazards and Disasters (Hazards SEES) program.

Published
2015
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146. The future of scientific workflows.

Author

Deelman, Ewa, Peterka, Tom, Altintas, Ilkay, Carothers, Christopher D., van Dam, Kerstin Kleese, Moreland, Kenneth, Parashar, Manish, Ramakrishnan, Lavanya, Taufer, Michela, and Vetter, Jeffrey

Subjects
AUTOMATION, WORKFLOW management, COMPUTER scientists, COMPUTER systems, DISTRIBUTED computing
Abstract

Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. This article is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science. [ABSTRACT FROM AUTHOR]

Published
2018
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147. EarthCube RoadMap: CROSS-DOMAIN INTEROPERABILITY

Author

Zaslavsky, Ilya, Altintas, Ilkay, Arctur, David, Brotzge, Jerry, Couch, Alva, Domenico, Ben, Hooper, Rick, Lehnert, Kerstin, Murphy, Philip, Nativi, Stefano, Plale, Beth, Richard, Steve, Stocks, Karen, and Valentine, David

Subjects
interoperability, Earthcube
Abstract

The rationale for cross-domain interoperability is to facilitate more accurate modeling and understanding of the Earth System, which requires integration of information about the solid earth, hydrosphere, atmosphere, biosphere, and interactions between these domains. Interdisciplinary research requires utilization of datasets that have been collected and compiled by various researchers at different times and places. Often data are applied to analyses different from those for which they were originally collected. Enabling sound scientific application of such cross-domain datasets requires that they are well documented, that they can be found, and that the information contained in them can be transformed into a form that can be used. Fostering cross-domain interoperability thus requires support for dataset documentation and curation from the beginning of its life cycle; development of discovery mechanisms that operate in a federated system of catalogs with different domain contexts; tools to support data exploration and manipulation to extract the desired information; a social framework for cross-domain networking between researchers needing to understand each other’s data; and a governance system to provide direction, decision-making, and authority for prioritizing and developing the necessary specifications and tools. In this document we present a roadmap for developing these capabilities in the context of the larger EarthCube framework., EarthCube roadmap prepared 2011-2012, apparently no longer available from EarthCube repositories.

Published
2012
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148. FlowGate

Author

Qian, Yu, primary, Scheuermann, Richard H., additional, Kim, Hyunsoo, additional, Purawat, Shweta, additional, Wang, Jianwu, additional, Stanton, Rick, additional, Lee, Alexandra, additional, Xu, Weijia, additional, Altintas, Ilkay, additional, and Sinkovits, Robert, additional

Published
2015
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150. The First Provenance Challenge

Author

Moreau, Luc, Ludaescher, Bertram, Altintas, Ilkay, Barga, Roger S., Bowers, Shawn, Callahan, Steven, Chin Jr., George, Clifford, Ben, Cohen, Shirley, Cohen-Boulakia, Sarah, Davidson, Susan, Deelman, Ewa, Digiampietri, Luciano, Foster, Ian, Freire, Juliana, Frew, James, Futrelle, Joe, Gibson, Tara, Gil, Yolanda, Goble, Carole, Golbeck, Jennifer, Groth, Paul, Holland, David A., Jiang, Sheng, Kim, Jihie, Koop, David, Krenek, Ales, McPhillips, Timothy, Mehta, Gaurang, Miles, Simon, Metzger, Dominic, Munroe, Steve, Myers, Jim, Plale, Beth, Podhorszki, Norbert, Ratnakar, Varun, Santos, Emanuele, Scheidegger, Carlos, Schuchardt, Karen, Seltzer, Margo, Simmhan, Yogesh L., Silva, Claudio, Slaughter, Peter, Stephan, Eric, Stevens, Robert, Turi, Daniele, Vo, Huy, Wilde, Mike, and Zhao, Jun

Abstract

The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarise the participants contributions.

Published
2008

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