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 language | English |
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Title of host publication | ICEIS 2019 - Proceedings of the 21st International Conference on Enterprise Information Systems |
Editors | Alexander Brodsky, Joaquim Filipe, Michal Smialek, Slimane Hammoudi |
Publisher | SCITEPRESS-Science and Technology Publications, Lda. |
Pages | 213-220 |
Number of pages | 8 |
ISBN (Electronic) | 9789897583728 |
DOIs | |
Publication status | Published - 5 May 2019 |
Event | 21st International Conference on Enterprise Information Systems, (ICEIS2019) - Heraklion, Crete, Greece Duration: 3 May 2019 → 5 May 2019 http://www.iceis.org/?y=2019 |
Conference
Conference | 21st International Conference on Enterprise Information Systems, (ICEIS2019) |
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Abbreviated title | ICEIS2019 |
Country | Greece |
City | Heraklion, Crete |
Period | 3/05/19 → 5/05/19 |
Internet address |
Keywords
- Automatic Data Generation
- Business Process Model
- ERP
- Process Mining