Towards specification of a software architecture for cross-sectoral big data applications

Ioannis Arapakis; Yolanda Becerra; Omer Boehm; George Bravos; Vassilis Chatzigiannakis; Cesare Cugnasco; Giorgos Demetriou; Iliada Eleftheriou; Julien Etienne Mascolo; Lidija Fodor; Sotiris Ioannidis; Dusan Jakovetic; Leonidas Kallipolitis; Evangelia Kavakli; Despina Kopanaki; Nicolas Kourtellis; Mario Maawad Marcos; Ramon Martin De Pozuelo; Nemanja Milosevic; Giuditta Morandi; Enric Pages Montanera; Gerald Ristow; Rizos Sakellariou; Raul Sirvent; Srdjan Skrbic; Ilias Spais; Giorgos Vasiliadis; Michael Vinov

doi:10.1109/SERVICES.2019.00120

Towards specification of a software architecture for cross-sectoral big data applications

Ioannis Arapakis, Yolanda Becerra, Omer Boehm, George Bravos, Vassilis Chatzigiannakis, Cesare Cugnasco, Giorgos Demetriou, Iliada Eleftheriou, Julien Etienne Mascolo, Lidija Fodor, Sotiris Ioannidis, Dusan Jakovetic, Leonidas Kallipolitis, Evangelia Kavakli, Despina Kopanaki, Nicolas Kourtellis, Mario Maawad Marcos, Ramon Martin De Pozuelo, Nemanja Milosevic, Giuditta MorandiEnric Pages Montanera, Gerald Ristow, Rizos Sakellariou, Raul Sirvent, Srdjan Skrbic, Ilias Spais, Giorgos Vasiliadis, Michael Vinov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.

Original language	English
Title of host publication	Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019
Editors	Carl K. Chang, Peter Chen, Michael Goul, Katsunori Oyama, Stephan Reiff-Marganiec, Yanchun Sun, Shangguang Wang, Zhongjie Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	394-395
Number of pages	2
ISBN (Electronic)	9781728138510
DOIs	https://doi.org/10.1109/SERVICES.2019.00120
State	Published - Jul 2019
Externally published	Yes
Event	2019 IEEE World Congress on Services, SERVICES 2019 - Milan, Italy Duration: 8 Jul 2019 → 13 Jul 2019

Publication series

Name	Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019

Conference

Conference	2019 IEEE World Congress on Services, SERVICES 2019
Country/Territory	Italy
City	Milan
Period	8/07/19 → 13/07/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Big data as a self service solution
Big data value chain
Software architecture

ASJC Scopus subject areas

Hardware and Architecture
Information Systems and Management
Safety, Risk, Reliability and Quality
Management Science and Operations Research
Artificial Intelligence
Computer Networks and Communications
Information Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SERVICES.2019.00120

Cite this

Arapakis, I., Becerra, Y., Boehm, O., Bravos, G., Chatzigiannakis, V., Cugnasco, C., Demetriou, G., Eleftheriou, I., Etienne Mascolo, J., Fodor, L., Ioannidis, S., Jakovetic, D., Kallipolitis, L., Kavakli, E., Kopanaki, D., Kourtellis, N., Maawad Marcos, M., Martin De Pozuelo, R., Milosevic, N., ... Vinov, M. (2019). Towards specification of a software architecture for cross-sectoral big data applications. In C. K. Chang, P. Chen, M. Goul, K. Oyama, S. Reiff-Marganiec, Y. Sun, S. Wang, & Z. Wang (Eds.), Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019 (pp. 394-395). Article 8817160 (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SERVICES.2019.00120

Arapakis, Ioannis ; Becerra, Yolanda ; Boehm, Omer et al. / Towards specification of a software architecture for cross-sectoral big data applications. Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019. editor / Carl K. Chang ; Peter Chen ; Michael Goul ; Katsunori Oyama ; Stephan Reiff-Marganiec ; Yanchun Sun ; Shangguang Wang ; Zhongjie Wang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 394-395 (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019).

@inproceedings{3fe90a838c2f429fbd9fe79a1ffa92c2,

title = "Towards specification of a software architecture for cross-sectoral big data applications",

abstract = "The proliferation of Big Data applications puts pressure on improving and optimizing the handling of diverse datasets across different domains. Among several challenges, major difficulties arise in data-sensitive domains like banking, telecommunications, etc., where strict regulations make very difficult to upload and experiment with real data on external cloud resources. In addition, most Big Data research and development efforts aim to address the needs of IT experts, while Big Data analytics tools remain unavailable to non-expert users to a large extent. In this paper, we report on the work-in-progress carried out in the context of the H2020 project I-BiDaaS (Industrial-Driven Big Data as a Self-service Solution) which aims to address the above challenges. The project will design and develop a novel architecture stack that can be easily configured and adjusted to address cross-sectoral needs, helping to resolve data privacy barriers in sensitive domains, and at the same time being usable by non-experts. This paper discusses and motivates the need for Big Data as a self-service, reviews the relevant literature, and identifies gaps with respect to the challenges described above. We then present the I-BiDaaS paradigm for Big Data as a self-service, position it in the context of existing references, and report on initial work towards the conceptual specification of the I-BiDaaS software architecture.",

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author = "Ioannis Arapakis and Yolanda Becerra and Omer Boehm and George Bravos and Vassilis Chatzigiannakis and Cesare Cugnasco and Giorgos Demetriou and Iliada Eleftheriou and {Etienne Mascolo}, Julien and Lidija Fodor and Sotiris Ioannidis and Dusan Jakovetic and Leonidas Kallipolitis and Evangelia Kavakli and Despina Kopanaki and Nicolas Kourtellis and {Maawad Marcos}, Mario and {Martin De Pozuelo}, Ramon and Nemanja Milosevic and Giuditta Morandi and {Pages Montanera}, Enric and Gerald Ristow and Rizos Sakellariou and Raul Sirvent and Srdjan Skrbic and Ilias Spais and Giorgos Vasiliadis and Michael Vinov",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE World Congress on Services, SERVICES 2019 ; Conference date: 08-07-2019 Through 13-07-2019",

year = "2019",

month = jul,

doi = "10.1109/SERVICES.2019.00120",

language = "English",

series = "Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "394--395",

editor = "Chang, {Carl K.} and Peter Chen and Michael Goul and Katsunori Oyama and Stephan Reiff-Marganiec and Yanchun Sun and Shangguang Wang and Zhongjie Wang",

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Arapakis, I, Becerra, Y, Boehm, O, Bravos, G, Chatzigiannakis, V, Cugnasco, C, Demetriou, G, Eleftheriou, I, Etienne Mascolo, J, Fodor, L, Ioannidis, S, Jakovetic, D, Kallipolitis, L, Kavakli, E, Kopanaki, D, Kourtellis, N, Maawad Marcos, M, Martin De Pozuelo, R, Milosevic, N, Morandi, G, Pages Montanera, E, Ristow, G, Sakellariou, R, Sirvent, R, Skrbic, S, Spais, I, Vasiliadis, G & Vinov, M 2019, Towards specification of a software architecture for cross-sectoral big data applications. in CK Chang, P Chen, M Goul, K Oyama, S Reiff-Marganiec, Y Sun, S Wang & Z Wang (eds), Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019., 8817160, Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019, Institute of Electrical and Electronics Engineers Inc., pp. 394-395, 2019 IEEE World Congress on Services, SERVICES 2019, Milan, Italy, 8/07/19. https://doi.org/10.1109/SERVICES.2019.00120

Towards specification of a software architecture for cross-sectoral big data applications. / Arapakis, Ioannis; Becerra, Yolanda; Boehm, Omer et al.
Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019. ed. / Carl K. Chang; Peter Chen; Michael Goul; Katsunori Oyama; Stephan Reiff-Marganiec; Yanchun Sun; Shangguang Wang; Zhongjie Wang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 394-395 8817160 (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Morandi, Giuditta

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AU - Ristow, Gerald

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Arapakis I, Becerra Y, Boehm O, Bravos G, Chatzigiannakis V, Cugnasco C et al. Towards specification of a software architecture for cross-sectoral big data applications. In Chang CK, Chen P, Goul M, Oyama K, Reiff-Marganiec S, Sun Y, Wang S, Wang Z, editors, Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 394-395. 8817160. (Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019). doi: 10.1109/SERVICES.2019.00120

Towards specification of a software architecture for cross-sectoral big data applications

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this