Rafii, Fadoua, González-Vélez, Horacio and Chis, Adriana E. (2023) Automatic FAIR Provenance Collection and Visualization for Time Series. In: Companion of the 2023 ACM/SPEC International Conference on Performance Engineering (ICPE '23 Companion). Association for Computing Machinery, Coimbra, Portugal, pp. 331-336. ISBN 9798400700729
Full text not available from this repository.Abstract
Provenance provides data lineage and history of different transformations applied to a dataset. A complete trace of data provenance can enable the reanalysis, reproducibility, and reusability of features, which are essential for validating results and extending them in many projects. Open time series datasets are readily accessible and discoverable, but their full reproducibility and reusability require clear metadata provenance. This paper introduces an assessment of provenance variables using an algorithm for collecting FAIR (Findable, Accessible, Interoperable, Reusable) characteristics in open time series and generating an associated provenance graph. We have evaluated the FAIRness of provenance traces by automatically mapping their properties to a provenance data model graph for a case study employing open time series from weather stations. Our approach arguably enables researchers to analyse time series datasets with similar characteristics, prompting new research questions, insights, and investigations. As a result, this approach has the potential to promote reusability and reproducibility, which are critical factors in scientific research.
Item Type: | Book Section |
---|---|
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Electronic computers. Computer science > Computer Systems > Information Storage and Retrieval Systems T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science > Computer Systems > Information Storage and Retrieval Systems H Social Sciences > HM Sociology > Information Science |
Divisions: | School of Computing > Staff Research and Publications |
Depositing User: | Tamara Malone |
Date Deposited: | 14 Apr 2023 14:21 |
Last Modified: | 14 Apr 2023 14:21 |
URI: | https://norma.ncirl.ie/id/eprint/6453 |
Actions (login required)
View Item |