Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Vijay Gadepally"'
Autor:
Yasar Khan, Antoine Zimmermann, Alokkumar Jha, Vijay Gadepally, Mathieu D'Aquin, Ratnesh Sahay
Publikováno v:
IEEE Access, Vol 7, Pp 9598-9617 (2019)
Data retrieval systems are facing a paradigm shift due to the proliferation of specialized data storage engines (SQL, NoSQL, Column Stores, MapReduce, Data Stream, and Graph) supported by varied data models (CSV, JSON, RDB, RDF, and XML). One immedia
Externí odkaz:
https://doaj.org/article/493742f50d55441b8ae2350fbff75eaf
Publikováno v:
Journal of Advanced Transportation, Vol 2017 (2017)
We present a framework for estimation of long term driver behavior for autonomous vehicles and vehicle safety systems. The Hybrid State System and Hidden Markov Model (HSS+HMM) system discussed in this article is capable of describing the hybrid char
Externí odkaz:
https://doaj.org/article/9bd894830f1049e2ae34581e4de9aa3a
Publikováno v:
Proceedings of the 13th Symposium on Cloud Computing.
Autor:
Athinagoras Skiadopoulos, Qian Li, Peter Kraft, Kostis Kaffes, Daniel Hong, Shana Mathew, David Bestor, Michael Cafarella, Vijay Gadepally, Goetz Graefe, Jeremy Kepner, Christos Kozyrakis, Tim Kraska, Michael Stonebraker, Lalith Suresh, Matei Zaharia
Publikováno v:
Proceedings of the VLDB Endowment. 15:21-30
This paper lays out the rationale for building a completely new operating system (OS) stack. Rather than build on a single node OS together with separate cluster schedulers, distributed filesystems, and network managers, we argue that a distributed t
Autor:
El Kindi Rezig, Vijay Gadepally, Michael Stonebraker, Benjamin Price, Anna Fariha, Allan Vanterpool, Anshul Bhandari
Publikováno v:
Proceedings of the VLDB Endowment. 14:2819-2822
In order to conduct analytical tasks, data scientists often need to find relevant data from an avalanche of sources (e.g., data lakes, large organizational databases). This effort is typically made in an ad hoc, non-systematic manner, which makes it
Autor:
Joseph McDonald, James M. Kurdzo, Phillip M. Stepanian, Mark Veillette, David Bestor, Michael Jones, Vijay Gadepally, Siddharth Samsi
Publikováno v:
2022 IEEE High Performance Extreme Computing Conference (HPEC).
Publikováno v:
2022 IEEE High Performance Extreme Computing Conference (HPEC).
Autor:
Lauren Milechin, Shana Hutchison, Hayden Jananthan, Jeremy Kepner, Benjamin A. Miller, Andrew Prout, Siddharth Samsi, Chuck Yee, Vijay Gadepally
Publikováno v:
Massive Graph Analytics ISBN: 9781003033707
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4181781bd7f0d910ec58ae3b09ba2b0b
https://doi.org/10.1201/9781003033707-25
https://doi.org/10.1201/9781003033707-25
Autor:
Jeremy Kepner, Kenjiro Cho, KC Claffy, Vijay Gadepally, Sarah McGuire, Lauren Milechin, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Michael Jones, Andrew Prout, Albert Reuther, Antonio Rosa, Siddharth Samsi, Charles Yee, Peter Michaleas
Publikováno v:
Massive Graph Analytics ISBN: 9781003033707
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe99b98c682a20ac669fa095762c813c
https://doi.org/10.1201/9781003033707-13
https://doi.org/10.1201/9781003033707-13
Autor:
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gomez-Bombarelli, Connor Coley, Vijay Gadepally
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::002098cccd1d6ce46ceb7274ad1ba1cd
https://doi.org/10.26434/chemrxiv-2022-3s512
https://doi.org/10.26434/chemrxiv-2022-3s512