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 proceedingConference proceedingpeer-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 languageEnglish
    Title of host publicationProceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
    EditorsWeiming Shen, Hugo Paredes, Junzhou Luo, Jean-Paul Barthes
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages146-151
    Number of pages6
    ISBN (Electronic)9781728103501, 9781728103495
    DOIs
    Publication statusPublished - 8 Aug 2019
    Event23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019 - Porto, Portugal
    Duration: 6 May 20198 May 2019

    Publication series

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

    Conference

    Conference23rd IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019
    CountryPortugal
    CityPorto
    Period6/05/198/05/19

    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

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