TY - GEN
T1 - Towards specification of a software architecture for cross-sectoral big data applications
AU - Arapakis, Ioannis
AU - Becerra, Yolanda
AU - Boehm, Omer
AU - Bravos, George
AU - Chatzigiannakis, Vassilis
AU - Cugnasco, Cesare
AU - Demetriou, Giorgos
AU - Eleftheriou, Iliada
AU - Etienne Mascolo, Julien
AU - Fodor, Lidija
AU - Ioannidis, Sotiris
AU - Jakovetic, Dusan
AU - Kallipolitis, Leonidas
AU - Kavakli, Evangelia
AU - Kopanaki, Despina
AU - Kourtellis, Nicolas
AU - Maawad Marcos, Mario
AU - Martin De Pozuelo, Ramon
AU - Milosevic, Nemanja
AU - Morandi, Giuditta
AU - Pages Montanera, Enric
AU - Ristow, Gerald
AU - Sakellariou, Rizos
AU - Sirvent, Raul
AU - Skrbic, Srdjan
AU - Spais, Ilias
AU - Vasiliadis, Giorgos
AU - Vinov, Michael
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Big data as a self service solution
KW - Big data value chain
KW - Software architecture
UR - http://www.scopus.com/inward/record.url?scp=85072763353&partnerID=8YFLogxK
U2 - 10.1109/SERVICES.2019.00120
DO - 10.1109/SERVICES.2019.00120
M3 - Conference contribution
AN - SCOPUS:85072763353
T3 - Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019
SP - 394
EP - 395
BT - Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Goul, Michael
A2 - Oyama, Katsunori
A2 - Reiff-Marganiec, Stephan
A2 - Sun, Yanchun
A2 - Wang, Shangguang
A2 - Wang, Zhongjie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE World Congress on Services, SERVICES 2019
Y2 - 8 July 2019 through 13 July 2019
ER -