@inproceedings{sirqueira2017software,
  title={A Software Framework for Data Provenance.},
  author={Sirqueira, Tassio Ferenzini Martins and Viana, Marx L and Lucena, Carlos},
  year = {2017},
  booktitle = {The 29th International Conference on Software Engineering & Knowledge Engineering (SEKE 2017)},
  editor = {KSI Research Inc. and Knowledge Systems Institute},
  pages = {615-618},
  doi = "https://doi.org/10.18293/SEKE2017-085",
  url = {https://www.researchgate.net/profile/Tassio_Sirqueira/publication/317351029_A_Software_Framework_for_Data_Provenance/links/5935b96c45851553b601b269/A-Software-Framework-for-Data-Provenance.pdf},
  abstract = "Data provenance refers to the historical record of the derivation of the data, allowing the reproduction of experiments, interpretation of results and identification of problems through the analysis of the processes that originated the data. Data provenance contributes to the evaluation of experiments. This paper presents a framework for data provenance using the W3C provenance data model, called PROV-DM. Such framework aims at contributing to, and facilitating, the collection, storage and retrieval of provenance data through a modeling and storage layer based on PROV-DM, yet is compatible with other representations of PROV such as PROV-O. To demonstrate the utilization of the framework, it was used in an IoT application that performs the gas classification to identify diseases."
}

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