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https://ceur-ws.org/Vol-3195/xpreface.pdf
Preface of the First Ph.D. Workshop on
Big Data Analytics from the LAMBDA Network
Damien Graux1 and Valentina Janev2
1
Inria, Université Côte d’Azur, CNRS, I3S, France damien.graux@inria.fr
2
Institut Mihajlo Pupin, University of Belgrade, Serbia
valentina.janev@institutepupin.com
Abstract. The Doctoral Workshop was organized with the aim to pro-
vide Ph.D. candidates with an opportunity to present their research
projects and their scientific questions to their peers and international
academic experts in the domain of Big Data analytics. Another goal of
the Workshop was to develop a supportive community within which stu-
dents can begin to develop their professional networks, interacting with
peers and senior scholars from the field.
This 1st edition, co-located with the LAMBDA Big Data Analytics sum-
mer school located in Belgrade (Serbia), gathered the community around
nine research publications, three talks and one invited keynote presenta-
tion. The event took place online on the 17th of June, 2021.
Keywords: Big Data · Analytics · Energy · Ph.D. Workshop · LAMBDA
The European H2020 LAMBDA Project
Big Data refers to data sets which have large size and complex structure.
The data size can range from dozens of terabytes to a few zettabytes and is
still growing [16]. Big Data Analytics, hence, refers to the strategy of analysing
large volumes of data that are gathered from a wide variety of sources, including
social networks, transaction records, videos, digital images and different kind of
sensors. In an attempt to support the European data economy policy [10], our
consortium proposed a training approach [13] and established the infrastructure
for collaborative work of teachers/trainers with Ph.D. students and other in-
terested parties such as industries. In particular, our activities are taking place
within the scope of the Horizon 2020 project named LAMBDA, standing for
“Learning, Applying, Multiplying Big Data Analytics”.
Our overall objectives are to stimulate scientific excellence and innovation
capacity, to increase the research capacities and to unlock the research potential
in the ICT area in the whole West Balkan region, turning the partner-institutes
into regional points of reference when it comes to multidisciplinary ICT com-
petence related to Big Data analytics. In early 2018, the consortium started
activities for improving the skills and competences for smart data management
Copyright © 2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
through a set of actions such as: the organization of international events (train-
ing, workshops, webinars, conferences), the development of a specific learning kit
about Big Data Analytics [13], or the publication of an open-access book1 [14].
In particular, to increase the audience and the availability of our initiative,
we developed an online knowledge repository to help students acquiring new
skills:
The learning materials that were produced are free, stored in a public repository
and available online [12] using the OpenCourseWare platform SlideWiki [15].
This project repository aims at facilitating the exchange of learning materials,
tools, project results and best practice between the international leading organi-
zations and research institutions and industry from the West Balkan countries.
Since 2019, among the various activities of the project, the LAMBDA con-
sortium organized three editions of the Big Data Analytics Summer School in
Belgrade (Serbia) where students were able to connect with experts and to re-
ceive lectures and advice from them. This year, during the 2021 edition of the
summer school, we organized for the first time a Ph.D. day as the community
is getting momentum, in order to give Ph.D. students a place to present their
current state of research efforts.
Scientific programme
The Ph.D. workshop started with the keynote entitled “Hints to Save Time when
dealing with Big Data” given by Dr. Damien Graux from Inria2 (France). He
described how to adopt strategies which should help Big Data practitioners to
fasten their processes and tasks [11]. In particular, Dr. Graux gave an overview
of techniques and methods to better define the scope and type of Big Data
the practitioners are dealing with. He gave indicators and rules to quickly know
whether some systems should be considered or not for specific use cases. Overall,
this keynote gave the audience in-depth details on practical use cases backed by
cutting-edge research techniques.
Then, a session on Semantic Web tools in the context of Big Data happened.
It was composed of two articles [4, 1]. Next a session dealing with big textual
resources was organized. Three papers were presented: Gjorgjevikj et al. on de-
tecting sustainable development indicators through text; Mishev et al. focused
on text-to-speech for Macedonian; and [6] proposed a blockchain-based platform
for logs of citizens’ consents. After the lunch break, the third and fourth sessions
gathered articles addressing energy challenges. Numerical tools for combustion
behaviors predict were presented in [7]. In [8] and [2], authors respectively dis-
cussed how to detect faults in distribution networks and how to detect events
in power cables. A machine learning based wind turbine production forecaster
1
Downloaded more than 65 000 times as of December 2021.
2
https://dgraux.github.io/
was then presented by Pujić and Janev. Efforts to develop a control platform
dedicated to renewable energy were discussed in [9]. Efficiently benchmarking
smart home challenges were explained [3]. And finally, a coordination platform
for handling emergencies and restoration of power grid was introduced [5].
Organizing Committee
– Heba Mohamed, University of Bonn, Germany
– Nikola Tomašević, Institute Mihajlo Pupin, Serbia
– Marko Batić, Institute Mihajlo Pupin, Serbia
International Doctoral Committee
– Valentina Janev, Institute Mihajlo Pupin, Serbia (Chair)
– Sanja Vraneš, Institute Mihajlo Pupin, Serbia
– Lazar Berbakov, Institute Mihajlo Pupin, Serbia
– Emanuel Sallinger, University of Oxford, UK
– Anastasia Dimou, imec and Ghent University, Belgium
– Diego Collarana, Fraunhofer IAIS, Germany
– Maria-Esther Vidal, TIB, Leibniz University Hannover, Germany
– Jens Lehmann, University of Bonn, Germany
– Damien Graux, Inria, Université Côte d’Azur, CNRS, I3S, France
– Hajira Jabeen, CEPLAS, Technische Universität Dresden, Germany
– Andrej Čampa, ComSensus, Slovenia
– Marcus Keane, National University of Ireland, Galway, Ireland
– Dimitar Trajanov, Ss. Cyril and Methodius Univ, Skopje, North Macedonia
– Johannes Stöckl, Austrian Institute of Technology, Austra
– Federico Seri, National University of Ireland, Galway, Ireland
– Luis Miguel Blanes Restoy, National University of Ireland, Galway, Ireland
– Brankica Pažun, School of Engineering Management, Serbia
– Neven Vrček, Faculty of Organization & Informatics, Univ of Zagreb, Croatia
– Paulo Lissa, National University of Ireland, Galway, Ireland
Acknowledgements
We would like to thank all the authors, reviewers, committee members and the
invited speaker for their contributions, support and commitment during this
particularly challenging year.
This event was supported by the European Union Horizon 2020 project LAMBDA
(Grant Agreement No. 809965). In addition, it received the sponsoring support
of the Ministry of Science and Technological Development of the Republic of
Serbia (No. 451-03-9/2021-14/200034) and the Science Fund of the Republic of
Serbia (Artemis, No.6527051).
Articles presented at the Ph.D. workshop
1. Draschner, C.F., Moghaddam, F.B., Lehmann, J., Jabeen, H.: Semantic analytics in
the palm of your browser. In: Proceedings of the 1st Ph.D. Workshop on Big Data
Analytics from the LAMBDA Network (2021)
2. Hudomalj, M.: Traveling-wave event detection and localization on power cables. In:
Proceedings of the 1st Ph.D. Workshop on Big Data Analytics from the LAMBDA
Network (2021)
3. Jelić, M., Pujić, D., Batić, M.: Energy efficiency benchmarking for smart homes. In:
Proceedings of the 1st Ph.D. Workshop on Big Data Analytics from the LAMBDA
Network (2021)
4. Moghaddam, F.B., Draschner, C.F., Lehmann, J., Jabeen, H.: Semantic Web anal-
ysis with flavor of micro-services. In: Proceedings of the 1st Ph.D. Workshop on Big
Data Analytics from the LAMBDA Network (2021)
5. Popadić, D., Batić, M.: Coordination platform for handling emergencies and restora-
tion of power grid. In: Proceedings of the 1st Ph.D. Workshop on Big Data Analytics
from the LAMBDA Network (2021)
6. Popović, M., Tomašević, N.: A blockchain-based platform for keeping logs of citizens’
consents. In: Proceedings of the 1st Ph.D. Workshop on Big Data Analytics from
the LAMBDA Network (2021)
7. Silva, J., Fraga, L., Teixeira, S., Teixeira, J.: Numerical tools developed to predict
the combustion behavior inside a 20 kW pellet boiler. In: Proceedings of the 1st
Ph.D. Workshop on Big Data Analytics from the LAMBDA Network (2021)
8. Sodin, D.: PMU-based fault localization in distribution networks. In: Proceedings of
the 1st Ph.D. Workshop on Big Data Analytics from the LAMBDA Network (2021)
9. Stanković, K., Jelić, M., Batić, M.: The cloud-based control platform for multi-
source renewable energy system. In: Proceedings of the 1st Ph.D. Workshop on Big
Data Analytics from the LAMBDA Network (2021)
References
10. Commission, E.: Building a European data economy (2017), https://ec.europa.
eu/digital-single-market/en/policies/building-european-data-economy
11. Graux, D.: Hints to save time when dealing with Big Data. In: Proceedings of the
1st Ph.D. Workshop on Big Data Analytics from the LAMBDA Network (2021)
12. Graux, D., Janev, V., Jabeen, H., Sallinger, E.: A Big Data learning platform
for the West Balkans and beyond. In: Proceedings of the 26th ACM Conference
on Innovation and Technology in Computer Science Education V. 2. pp. 617–618
(2021)
13. Graux, D., Janev, V., Jabeen, H., Sallinger, E.: Deploying a strategy to unlock Big
Data research and teaching activities in the West Balkan region. In: Proceedings
of the 26th ACM Conference on Innovation and Technology in Computer Science
Education V. 1. pp. 491–497 (2021)
14. Janev, V., Graux, D., Jabeen, H., Sallinger, E.: Knowledge graphs and Big Data
processing. Springer Nature (2020), https://link.springer.com/book/10.1007/
978-3-030-53199-7
15. Khalili, A., Auer, S., Tarasowa, D., Ermilov, I.: SlideWiki: elicitation and shar-
ing of corporate knowledge using presentations. In: Int. Conference on Knowledge
Engineering and Knowledge Management. pp. 302–316. Springer (2012)
16. Zhou, A.C., He, B.: Big Data and exascale computing. In: S. Sakr, A. Y. Zomaya
(eds) Encyclopedia of Big Data Technologies, Springer, Cham. (2019)