A model-based framework to automatically generate semi-real data for evaluating data analysis techniques

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    Abstract

    As data analysis techniques progress, the focus shifts from simple tabular data to more complex data at the level of business objects. Therefore, the evaluation of such data analysis techniques is far from trivial. However, due to confidentiality, most researchers are facing problems collecting available real data to evaluate their techniques. One alternative approach is to use synthetic data instead of real data, which leads to unconvincing results. In this paper, we propose a framework to automatically operate information systems (supporting operational processes) to generate semi-real data (i.e., “operations related data” exclusive of images, sound, video, etc.). This data have the same structure as the real data and are more realistic than traditional simulated data. A plugin is implemented to realize the framework for automatic data generation.

    Original languageEnglish
    Title of host publicationICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems
    EditorsAlexander Brodsky, Joaquim Filipe, Michal Smialek, Slimane Hammoudi
    PublisherSCITEPRESS-Science and Technology Publications, Lda.
    Pages213-220
    Number of pages8
    ISBN (Electronic)9789897583728
    DOIs
    Publication statusPublished - 5 May 2019
    Event21st International Conference on Enterprise Information Systems, (ICEIS2019) - Heraklion, Crete, Greece
    Duration: 3 May 20195 May 2019
    http://www.iceis.org/?y=2019

    Conference

    Conference21st International Conference on Enterprise Information Systems, (ICEIS2019)
    Abbreviated titleICEIS2019
    CountryGreece
    CityHeraklion, Crete
    Period3/05/195/05/19
    Internet address

    Keywords

    • Automatic Data Generation
    • Business Process Model
    • ERP
    • Process Mining

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