TY - GEN
T1 - A software framework for data provenance
AU - Sirqueira, Tassio
AU - Viana, Marx
AU - Nascimento, Nathalia
AU - Lucena, Carlos
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029533198&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029533198&partnerID=8YFLogxK
U2 - 10.18293/SEKE2017-085
DO - 10.18293/SEKE2017-085
M3 - Conference contribution
AN - SCOPUS:85029533198
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 615
EP - 618
BT - Proceedings - SEKE 2017
PB - Knowledge Systems Institute Graduate School
T2 - 29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
Y2 - 5 July 2017 through 7 July 2017
ER -