MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

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.

I: IEEE Big Data Service 2023, 2023, s. 1487-1494.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Doulkeridis, C, Santipantakis, G, Koutroumanis, N, Makridis, G, Koukos, V, Theodoropoulos, GS, Theodoridis, Y, Kyriazis, D, Kranas, P, Burgos, D, Jimenez-Peris, R, Duarte, M, Sakr, M, Graser, A, Heistracher, C, Torp, K, Chrysakis, IC, Orphanoudakis, T, Kapassa, E, Touloupou, M, Neises, J, Petrou, P, Karagiorgou, S, Catelli, R, Messina, D, Corrales Compagnucci, M & Falsetta, M 2023, 'MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data', IEEE Big Data Service 2023, s. 1487-1494. https://doi.org/10.1109/BigData59044.2023.10386539

APA

Doulkeridis, C., Santipantakis, G., Koutroumanis, N., Makridis, G., Koukos, V., Theodoropoulos, G. S., Theodoridis, Y., Kyriazis, D., Kranas, P., Burgos, D., Jimenez-Peris, R., Duarte, M., Sakr, M., Graser, A., Heistracher, C., Torp, K., Chrysakis, I. C., Orphanoudakis, T., Kapassa, E., ... Falsetta, M. (2023). MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data. IEEE Big Data Service 2023, 1487-1494. https://doi.org/10.1109/BigData59044.2023.10386539

Vancouver

Doulkeridis C, Santipantakis G, Koutroumanis N, Makridis G, Koukos V, Theodoropoulos GS o.a. MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data. IEEE Big Data Service 2023. 2023;1487-1494. https://doi.org/10.1109/BigData59044.2023.10386539

Author

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. / MobiSpaces : An Architecture for Energy-Efficient Data Spaces for Mobility Data. I: IEEE Big Data Service 2023. 2023 ; s. 1487-1494.

Bibtex

@inproceedings{0d65b40ed7e44c80b792e8128190995e,
title = "MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data",
abstract = "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.",
author = "Christos Doulkeridis and Georgios Santipantakis and Nikolaos Koutroumanis and George Makridis and Vasilis Koukos and Theodoropoulos, {George S.} and Yannis Theodoridis and Dimosthenis Kyriazis and Pavlos Kranas and Diego Burgos and Ricardo Jimenez-Peris and Mariana Duarte and Mahmoud Sakr and Anita Graser and Clemens Heistracher and Kristian Torp and Chrysakis, {Ioannis Chrysakis} and Theofanis Orphanoudakis and Evgenia Kapassa and Marios Touloupou and Juergen Neises and Petros Petrou and Sophia Karagiorgou and Rosario Catelli and Domenico Messina and {Corrales Compagnucci}, Marcelo and Matteo Falsetta",
year = "2023",
doi = "10.1109/BigData59044.2023.10386539",
language = "English",
pages = "1487--1494",
journal = "IEEE Big Data Service 2023",
note = "2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

}

RIS

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