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

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Keywords

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

Cite this

Li, G., de Carvalho, R. M., & van der Aalst, W. M. P. (2019). A model-based framework to automatically generate semi-real data for evaluating data analysis techniques. In A. Brodsky, J. Filipe, M. Smialek, & S. Hammoudi (Eds.), ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems (pp. 213-220). SCITEPRESS-Science and Technology Publications, Lda.. https://doi.org/10.5220/0007713702130220