Zobrazeno 1 - 10
of 1 361
pro vyhledávání: '"Karanasos A"'
Autor:
Latsios G, Synetos A, Leopoulou M, Stamatopoulou E, Koukopoulos P, Parisis C, Karanasos A, Fragkou P, Toutouzas K, Kanakakis J, Tsioufis K
Publikováno v:
Open Access Emergency Medicine, Vol Volume 14, Pp 63-75 (2022)
George Latsios,1,* Andreas Synetos,1,* Marianna Leopoulou,2 Evaggelia Stamatopoulou,3 Panagiotis Koukopoulos,4 Charalambos Parisis,5 Antonios Karanasos,1 Paraskevi Fragkou,6 Konstantinos Toutouzas,1 John Kanakakis,7 Kostas Tsioufis1 1First De
Externí odkaz:
https://doaj.org/article/88807839da0a45a7aa030cd7d5f9e6fa
Autor:
Psallidas, Fotis, Agrawal, Ashvin, Sugunan, Chandru, Ibrahim, Khaled, Karanasos, Konstantinos, Camacho-Rodríguez, Jesús, Floratou, Avrilia, Curino, Carlo, Ramakrishnan, Raghu
Provenance encodes information that connects datasets, their generation workflows, and associated metadata (e.g., who or when executed a query). As such, it is instrumental for a wide range of critical governance applications (e.g., observability and
Externí odkaz:
http://arxiv.org/abs/2210.14047
Autor:
Park, Kwanghyun, Saur, Karla, Banda, Dalitso, Sen, Rathijit, Interlandi, Matteo, Karanasos, Konstantinos
Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (ML) part in
Externí odkaz:
http://arxiv.org/abs/2206.00136
Autor:
He, Dong, Nakandala, Supun, Banda, Dalitso, Sen, Rathijit, Saur, Karla, Park, Kwanghyun, Curino, Carlo, Camacho-Rodríguez, Jesús, Karanasos, Konstantinos, Interlandi, Matteo
Publikováno v:
Proceedings of the VLDB Endowment, 15(11): 2811 - 2825, 2022
The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major clou
Externí odkaz:
http://arxiv.org/abs/2203.01877
For the large family of ARMA models with variable coefficients (TV-ARMA), either deterministic or stochastic, we provide an explicit and computationally tractable representation based on the general solution of the associated linear difference equati
Externí odkaz:
http://arxiv.org/abs/2110.06168
Autor:
Zhu, Yiwen, Krishnan, Subru, Karanasos, Konstantinos, Tarte, Isha, Power, Conor, Modi, Abhishek, Kumar, Manoj, Zhang, Deli, Muthyala, Kartheek, Jurgens, Nick, Sakalanaga, Sarvesh, Darbha, Sudhir, Iyer, Minu, Agarwal, Ankita, Curino, Carlo
Microsoft's internal big-data infrastructure is one of the largest in the world -- with over 300k machines running billions of tasks from over 0.6M daily jobs. Operating this infrastructure is a costly and complex endeavor, and efficiency is paramoun
Externí odkaz:
http://arxiv.org/abs/2106.11445
Autor:
Nikolaos Ktenopoulos, Odysseas Katsaros, Anastasios Apostolos, Maria Drakopoulou, Grigorios Tsigkas, Constantinos Tsioufis, Periklis Davlouros, Konstantinos Toutouzas, Antonios Karanasos
Publikováno v:
Life, Vol 14, Iss 7, p 842 (2024)
The emergence of percutaneous treatment options provides novel therapeutic alternatives for older and feeble patients who are at high risk for any surgical procedure. The purpose of our review was to offer an up-to-date analysis of the rapidly expand
Externí odkaz:
https://doaj.org/article/b9217cb1aaf24dcfa4ecabf490bea12f
Autor:
Nakandala, Supun, Saur, Karla, Yu, Gyeong-In, Karanasos, Konstantinos, Curino, Carlo, Weimer, Markus, Interlandi, Matteo
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply untenable. Model scoring, the process of obtaining predictions from a train
Externí odkaz:
http://arxiv.org/abs/2010.04804
Autor:
Psallidas, Fotis, Zhu, Yiwen, Karlas, Bojan, Interlandi, Matteo, Floratou, Avrilia, Karanasos, Konstantinos, Wu, Wentao, Zhang, Ce, Krishnan, Subru, Curino, Carlo, Weimer, Markus
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This qu
Externí odkaz:
http://arxiv.org/abs/1912.09536
Autor:
Karanasos, Konstantinos, Interlandi, Matteo, Xin, Doris, Psallidas, Fotis, Sen, Rathijit, Park, Kwanghyun, Popivanov, Ivan, Nakandal, Supun, Krishnan, Subru, Weimer, Markus, Yu, Yuan, Ramakrishnan, Raghu, Curino, Carlo
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference. The RDBMS provides a n
Externí odkaz:
http://arxiv.org/abs/1911.00231