Data analysis on big data applications with small samples and incomplete information

Soizic Linford; Benjamin Bogdanovic; Kuo Ming Chao; Sladana Jankovic; Vladislav Maras; Mirjana Bugarinovic; Ilias Trochidis

doi:10.1109/CSCWD.2019.8791927

Data analysis on big data applications with small samples and incomplete information

Soizic Linford, Benjamin Bogdanovic, Kuo Ming Chao, Sladana Jankovic, Vladislav Maras, Mirjana Bugarinovic, Ilias Trochidis

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

Abstract

The EU and other public organizations at different levels of national and local government across the world have funded and invested in numerous research and development projects on big data transport applications over last few years. The mid and long term effectiveness of these applications is very difficult to measure, and the benefits and usability of these applications are not easy to calculate. NOESIS, funded under EU H2020 program, aims to design a decision supported tool by gathering and analyzing these applications as use cases to formulate sufficient knowledge for policy makers to make informed decisions for their big data transport applications. The challenges in this work are associated with a small number of samples, with incomplete information, but having a good size of features that need to be analyzed to make a confident enough recommendation. This paper reports various statistical and machine learning approaches used to address these challenges and their results.

Original language	English
Title of host publication	Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
Editors	Weiming Shen, Hugo Paredes, Junzhou Luo, Jean-Paul Barthes
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	146-151
Number of pages	6
ISBN (Electronic)	9781728103501, 9781728103495
DOIs	https://doi.org/10.1109/CSCWD.2019.8791927
Publication status	Published - 8 Aug 2019
Event	23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019 - Porto, Portugal Duration: 6 May 2019 → 8 May 2019

Publication series

Name	Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019

Conference

Conference	23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
Country/Territory	Portugal
City	Porto
Period	6/05/19 → 8/05/19

Keywords

Big data
Machine learning
Multivariate regression
Random forest

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Human-Computer Interaction
Information Systems
Information Systems and Management

Access to Document

10.1109/CSCWD.2019.8791927

Cite this

Linford, S., Bogdanovic, B., Chao, K. M., Jankovic, S., Maras, V., Bugarinovic, M., & Trochidis, I. (2019). Data analysis on big data applications with small samples and incomplete information. In W. Shen, H. Paredes, J. Luo, & J.-P. Barthes (Eds.), Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019 (pp. 146-151). Article 8791927 (Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCWD.2019.8791927

Data analysis on big data applications with small samples and incomplete information. / Linford, Soizic; Bogdanovic, Benjamin; Chao, Kuo Ming et al.
Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019. ed. / Weiming Shen; Hugo Paredes; Junzhou Luo; Jean-Paul Barthes. Institute of Electrical and Electronics Engineers Inc., 2019. p. 146-151 8791927 (Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019).

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

Linford, S, Bogdanovic, B, Chao, KM, Jankovic, S, Maras, V, Bugarinovic, M & Trochidis, I 2019, Data analysis on big data applications with small samples and incomplete information. in W Shen, H Paredes, J Luo & J-P Barthes (eds), Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019., 8791927, Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019, Institute of Electrical and Electronics Engineers Inc., pp. 146-151, 23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019, Porto, Portugal, 6/05/19. https://doi.org/10.1109/CSCWD.2019.8791927

Linford S, Bogdanovic B, Chao KM, Jankovic S, Maras V, Bugarinovic M et al. Data analysis on big data applications with small samples and incomplete information. In Shen W, Paredes H, Luo J, Barthes JP, editors, Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 146-151. 8791927. (Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019). doi: 10.1109/CSCWD.2019.8791927

Linford, Soizic ; Bogdanovic, Benjamin ; Chao, Kuo Ming et al. / Data analysis on big data applications with small samples and incomplete information. Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019. editor / Weiming Shen ; Hugo Paredes ; Junzhou Luo ; Jean-Paul Barthes. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 146-151 (Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019).

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Data analysis on big data applications with small samples and incomplete information

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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