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

Christos  Doulkeridis; Georgios M. Santipantakis; Nikolaos Koutroumanis; George Makridis; Vasilis Koukos; George Theodoropoulos; Yannis Theodoridis; Dimosthenis Kyriazis; Pavlos Kranas; Diego Burgos; Ricardo Jimenez-Peris; Mariana M G Duarte; Mahmoud Sakr; Esteban Zimányi; Anita Graser; Clemens Heistracher; Kristian Torp; Ioannis Chrysakis; Theofanis Orphanoudakis; Evgenia Kapassa; Marios Touloupou; Jürgen Neises; Petros Petrou; Sophia Karagiorgou; Rosario Catelli; Domenico Messina; Marcelo Corrales Compagnucci; Matteo Falsetta

doi:10.1109/BigData59044.2023.10386539

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

Christos Doulkeridis
, Georgios M. Santipantakis
, Nikolaos Koutroumanis
, George Makridis
, Vasilis Koukos
, George Theodoropoulos
, Yannis Theodoridis
, Dimosthenis Kyriazis
, Pavlos Kranas
, Diego Burgos
, Ricardo Jimenez-Peris
, Mariana M G Duarte
, Mahmoud Sakr
, Esteban Zimányi
, Anita Graser
, Clemens Heistracher
, Kristian Torp
, Ioannis Chrysakis
, Theofanis Orphanoudakis
, Evgenia Kapassa

Marios Touloupou, Jürgen Neises, Petros Petrou, Sophia Karagiorgou, Rosario Catelli, Domenico Messina, Marcelo Corrales Compagnucci, Matteo Falsetta

Data Science & Artifical Intelligence

University of Piraeus
LeanXcale
Université libre de Bruxelles
Aalborg University
Netcompany-Intrasoft
Ghent University
University Hospitals Leuven, KU Leuven
University of Nicosia
Fujitsu
Ubitech Ltd.
Engineering Ingegneria Informatica S.p.A.
White Label Consultancy
GFT Technologies SE

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

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 data sets, 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.

Original language	English
Title of host publication	Proceedings of IEEE BigDataService 2023
Subtitle of host publication	The 9th IEEE International Conference on Big Data Computing Service and Machine Learning Applications
Pages	1487 - 1494
ISBN (Electronic)	979-8-3503-2445-7
DOIs	https://doi.org/10.1109/BigData59044.2023.10386539
Publication status	Published - 2023
Event	2023 IEEE International Conference on Big Data (BigData) - Sorrento, Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Conference

Conference	2023 IEEE International Conference on Big Data (BigData)
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Research Field

Former Research Field - Data Science

Keywords

Mobility
Mobility data
data analytics
Data Spaces

Access to Document

10.1109/BigData59044.2023.10386539

Cite this

Doulkeridis , C., Santipantakis, G. M., Koutroumanis, N., Makridis, G., Koukos, V., Theodoropoulos, G., Theodoridis, Y., Kyriazis, D., Kranas, P., Burgos, D., Jimenez-Peris, R., Duarte, M. M. G., Sakr, M., Zimányi, E., Graser, A., Heistracher, C., Torp, K., Chrysakis, I., Orphanoudakis, T., ... Falsetta, M. (2023). MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data. In Proceedings of IEEE BigDataService 2023: The 9th IEEE International Conference on Big Data Computing Service and Machine Learning Applications (pp. 1487 - 1494) https://doi.org/10.1109/BigData59044.2023.10386539

@inproceedings{e82fcb89e79c44cbaeedcf8801ce2cdd,

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 data sets, 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.",

keywords = "Mobility, Mobility data, data analytics, Data Spaces",

author = "Christos Doulkeridis and Santipantakis, \{Georgios M.\} and Nikolaos Koutroumanis and George Makridis and Vasilis Koukos and George Theodoropoulos and Yannis Theodoridis and Dimosthenis Kyriazis and Pavlos Kranas and Diego Burgos and Ricardo Jimenez-Peris and Duarte, \{Mariana M G\} and Mahmoud Sakr and Esteban Zim{\'a}nyi and Anita Graser and Clemens Heistracher and Kristian Torp and Ioannis Chrysakis and Theofanis Orphanoudakis and Evgenia Kapassa and Marios Touloupou and J{\"u}rgen 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",

isbn = "979-8-3503-2446-4",

pages = "1487 -- 1494",

booktitle = "Proceedings of IEEE BigDataService 2023",

note = "2023 IEEE International Conference on Big Data (BigData) ; Conference date: 15-12-2023 Through 18-12-2023",

}

Doulkeridis , C, Santipantakis, GM, Koutroumanis, N, Makridis, G, Koukos, V, Theodoropoulos, G, Theodoridis, Y, Kyriazis, D, Kranas, P, Burgos, D, Jimenez-Peris, R, Duarte, MMG, Sakr, M, Zimányi, E, Graser, A, Heistracher, C, Torp, K, Chrysakis, I, 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. in Proceedings of IEEE BigDataService 2023: The 9th IEEE International Conference on Big Data Computing Service and Machine Learning Applications. pp. 1487 - 1494, 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386539

MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data. / Doulkeridis , Christos ; Santipantakis, Georgios M.; Koutroumanis, Nikolaos et al.
Proceedings of IEEE BigDataService 2023: The 9th IEEE International Conference on Big Data Computing Service and Machine Learning Applications. 2023. p. 1487 - 1494.

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

TY - GEN

T1 - MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data

AU - Doulkeridis , Christos

AU - Santipantakis, Georgios M.

AU - Koutroumanis, Nikolaos

AU - Makridis, George

AU - Koukos, Vasilis

AU - Theodoropoulos, George

AU - Theodoridis, Yannis

AU - Kyriazis, Dimosthenis

AU - Kranas, Pavlos

AU - Burgos, Diego

AU - Jimenez-Peris, Ricardo

AU - Duarte, Mariana M G

AU - Sakr, Mahmoud

AU - Zimányi, Esteban

AU - Graser, Anita

AU - Heistracher, Clemens

AU - Torp, Kristian

AU - Chrysakis, Ioannis

AU - Orphanoudakis, Theofanis

AU - Kapassa, Evgenia

AU - Touloupou, Marios

AU - Neises, Jürgen

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 data sets, 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 data sets, 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.

KW - Mobility

KW - Mobility data

KW - data analytics

KW - Data Spaces

U2 - 10.1109/BigData59044.2023.10386539

DO - 10.1109/BigData59044.2023.10386539

M3 - Conference Proceedings with Oral Presentation

SN - 979-8-3503-2446-4

SP - 1487

EP - 1494

BT - Proceedings of IEEE BigDataService 2023

T2 - 2023 IEEE International Conference on Big Data (BigData)

Y2 - 15 December 2023 through 18 December 2023

ER -

Doulkeridis C, Santipantakis GM, Koutroumanis N, Makridis G, Koukos V, Theodoropoulos G et al. MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data. In Proceedings of IEEE BigDataService 2023: The 9th IEEE International Conference on Big Data Computing Service and Machine Learning Applications. 2023. p. 1487 - 1494 doi: 10.1109/BigData59044.2023.10386539

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

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