Zobrazeno 1 - 10
of 417
pro vyhledávání: '"Papaemmanouil, A."'
Publikováno v:
AIDB@VLDB 2022 Proceedings of 4th International Workshop on Applied AI for Database Systems and Applications
Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing
Externí odkaz:
http://arxiv.org/abs/2205.14323
Autor:
Dudjak, Viktorija, Neves, Diana, Alskaif, Tarek, Khadem, Shafi, Pena-Bello, Alejandro, Saggese, Pietro, Bowler, Benjamin, Andoni, Merlinda, Bertolini, Marina, Zhou, Yue, Lormeteau, Blanche, Mustafa, Mustafa A., Wang, Yingjie, Francis, Christina, Zobiri, Fairouz, Parra, David, Papaemmanouil, Antonios
In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources. Some of the most researched mech
Externí odkaz:
http://arxiv.org/abs/2103.16137
Autor:
Xenophon Theodoridis, Michail Chourdakis, Androniki Papaemmanouil, Stavroula Chaloulakou, Niki Papageorgiou, Athina Vasiliki Georgakou, Georgios Chatzis, Areti Triantafyllou
Publikováno v:
Life, Vol 14, Iss 9, p 1210 (2024)
Vascular aging, marked by alterations in the structure and function of blood vessels, including heightened arterial stiffness and impaired endothelial function, is linked to a higher likelihood of developing cardiovascular and age-associated patholog
Externí odkaz:
https://doaj.org/article/37a637286372410ca4b28a146989b608
Autor:
Domvri, Kalliopi, Yaremenko, Alexey V., Apostolopoulos, Apostolos, Petanidis, Savvas, Karachrysafi, Sofia, Pastelli, Nikoleta, Papamitsou, Theodora, Papaemmanouil, Styliani, Lampaki, Sofia, Porpodis, Konstantinos
Publikováno v:
In Heliyon 15 March 2024 10(5)
Autor:
Kalliopi Domvri, Alexey V. Yaremenko, Apostolos Apostolopoulos, Savvas Petanidis, Sofia Karachrysafi, Nikoleta Pastelli, Theodora Papamitsou, Styliani Papaemmanouil, Sofia Lampaki, Konstantinos Porpodis
Publikováno v:
Heliyon, Vol 10, Iss 5, Pp e27208- (2024)
Lung cancer is a leading cause of cancer-related deaths globally, includes small cell lung cancer (SCLC), characterized by its aggressive nature and advanced disease at diagnosis. However, the identification of reliable biomarkers for SCLC has proven
Externí odkaz:
https://doaj.org/article/f9d8f32ee6e946239b98fbc6f2a726f2
Publikováno v:
AIDB@VLDB 2020 Proceedings of the 2nd International Workshop on Applied AI for Database Systems and Applications
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introduce SmartQueue, a learned scheduler that leverages overlapping data re
Externí odkaz:
http://arxiv.org/abs/2007.10568
Data-driven comparison of federated learning and model personalization for electric load forecasting
Publikováno v:
In Energy and AI October 2023 14
Data-driven comparison of federated learning and model personalization for electric load forecasting
Publikováno v:
Energy and AI, Vol 14, Iss , Pp 100253- (2023)
Residential short-term electric load forecasting is essential in modern decentralized power systems. Load forecasting methods mostly rely on neural networks and require access to private and sensitive electric load data for model training. Convention
Externí odkaz:
https://doaj.org/article/9e5d5cbd4cc84438b2c05d587f71fe01
Autor:
Marcus, Ryan, Negi, Parimarjan, Mao, Hongzi, Zhang, Chi, Alizadeh, Mohammad, Kraska, Tim, Papaemmanouil, Olga, Tatbul, Nesime
Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and
Externí odkaz:
http://arxiv.org/abs/1904.03711
Autor:
Marcus, Ryan, Papaemmanouil, Olga
Query performance prediction, the task of predicting the latency of a query, is one of the most challenging problem in database management systems. Existing approaches rely on features and performance models engineered by human experts, but often fai
Externí odkaz:
http://arxiv.org/abs/1902.00132