Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Raju Pavuluri"'
Autor:
Jayant Kalagnanam, Dzung T. Phan, Pavankumar Murali, Lam M. Nguyen, Nianjun Zhou, Dharmashankar Subramanian, Raju Pavuluri, Xiang Ma, Crystal Lui, Giovane Cesar da Silva
Publikováno v:
INFORMS Journal on Applied Analytics. 52:363-378
In this paper, we propose a site-wide lead advisor, which is an artificial intelligence–based prediction and set-point recommendation engine, by combining the use of machine learning with optimization techniques. It provides operational set-point r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a98ef34b897841a44c3f2e07822b374
https://doi.org/10.1287/inte.2022.1121
https://doi.org/10.1287/inte.2022.1121
Publikováno v:
5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4216249b7b33a2087cb3a06d61360e01
https://doi.org/10.1145/3493700.3493711
https://doi.org/10.1145/3493700.3493711
Autor:
Ma Xiang, Giovane Cesar Da Silva, Jayant R. Kalagnanam, Raju Pavuluri, Dharmashankar Subramanian, Pavankumar Murali, Nianjun Zhou
Publikováno v:
ICIOT
We address the problem of site-wide operational optimization of production plants in the context of Industry 4.0 with an emphasis on sensor data-driven approaches. A multi-plant production site is a complex network of plants and intermediate storage
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::29a849aec0e025f8264080d658683553
https://doi.org/10.1109/iciot.2019.00031
https://doi.org/10.1109/iciot.2019.00031
Publikováno v:
PPoPP
We present the multi-dimensional elasticity support in IBM Streams 4.3. Automatic operator fusion and dynamic threading were introduced in IBM Streams 4.2, which made it easier to map distributed stream processing to multicore systems through a low-c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53ac87c35ddfad0757d87b5dcf03c836
https://doi.org/10.1145/3293883.3301492
https://doi.org/10.1145/3293883.3301492
Autor:
Senthil Nathan, Yoonho Park, Volkmar Uhlig, Richard Pervin King, John F. Morar, Henrique Andrade, Philippe Selo, Michael John Elvery Spicer, Xiaolan J. Zhang, Bugra Gedik, Chitra Venkatramani, Randall Richard Schnier, Edward John Pring, Raju Pavuluri
Publikováno v:
SC-WHPCF
A stock market data processing system that can handle high data volumes at low latencies is critical to market makers. Such systems play a critical role in algorithmic trading, risk analysis, market surveillance, and many other related areas. We show
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e94dddd9fa54bf17ecaf2f9b3eaf1456
https://doi.org/10.1145/1645413.1645420
https://doi.org/10.1145/1645413.1645420