A software framework for data provenance

Tassio Sirqueira, Marx Viana, Nathalia Nascimento, Carlos Lucena

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    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.

    Original languageEnglish (US)
    Title of host publicationProceedings - SEKE 2017
    Subtitle of host publication29th International Conference on Software Engineering and Knowledge Engineering
    PublisherKnowledge Systems Institute Graduate School
    Pages615-618
    Number of pages4
    ISBN (Electronic)1891706411
    DOIs
    StatePublished - 2017
    Event29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017 - Pittsburgh, United States
    Duration: Jul 5 2017Jul 7 2017

    Publication series

    NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
    ISSN (Print)2325-9000
    ISSN (Electronic)2325-9086

    Conference

    Conference29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
    Country/TerritoryUnited States
    CityPittsburgh
    Period7/5/177/7/17

    All Science Journal Classification (ASJC) codes

    • Software

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