MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data
Research output: Contribution to journal › Conference article › Research › peer-review
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MobiSpaces : An Architecture for Energy-Efficient Data Spaces for Mobility Data. / Doulkeridis, Christos; Santipantakis, Georgios ; Koutroumanis, Nikolaos ; Makridis, George ; Koukos, Vasilis ; Theodoropoulos, George S. ; Theodoridis, Yannis ; Kyriazis, Dimosthenis ; Kranas, Pavlos ; Burgos, Diego ; Jimenez-Peris, Ricardo ; Duarte, Mariana ; Sakr, Mahmoud ; Graser, Anita ; Heistracher, Clemens ; Torp, Kristian ; Chrysakis, Ioannis Chrysakis; Orphanoudakis, Theofanis ; Kapassa, Evgenia ; Touloupou, Marios ; Neises, Juergen ; Petrou, Petros; Karagiorgou, Sophia ; Catelli, Rosario ; Messina, Domenico; Corrales Compagnucci, Marcelo; Falsetta, Matteo.
In: IEEE Big Data Service 2023, 2023, p. 1487-1494.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - MobiSpaces
T2 - 2023 IEEE International Conference on Big Data, BigData 2023
AU - Doulkeridis, Christos
AU - Santipantakis, Georgios
AU - Koutroumanis, Nikolaos
AU - Makridis, George
AU - Koukos, Vasilis
AU - Theodoropoulos, George S.
AU - Theodoridis, Yannis
AU - Kyriazis, Dimosthenis
AU - Kranas, Pavlos
AU - Burgos, Diego
AU - Jimenez-Peris, Ricardo
AU - Duarte, Mariana
AU - Sakr, Mahmoud
AU - Graser, Anita
AU - Heistracher, Clemens
AU - Torp, Kristian
AU - Chrysakis, Ioannis Chrysakis
AU - Orphanoudakis, Theofanis
AU - Kapassa, Evgenia
AU - Touloupou, Marios
AU - Neises, Juergen
AU - Petrou, Petros
AU - Karagiorgou, Sophia
AU - Catelli, Rosario
AU - Messina, Domenico
AU - Corrales Compagnucci, Marcelo
AU - Falsetta, Matteo
PY - 2023
Y1 - 2023
N2 - In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility datasets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.
AB - In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility datasets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.
U2 - 10.1109/BigData59044.2023.10386539
DO - 10.1109/BigData59044.2023.10386539
M3 - Conference article
SP - 1487
EP - 1494
JO - IEEE Big Data Service 2023
JF - IEEE Big Data Service 2023
Y2 - 15 December 2023 through 18 December 2023
ER -
ID: 345874035