Basically, if the provenance information about the Big Data cannot be stored securely, then there is no point in collecting it for auditing purposes. 2007 Glavic and Dittrich 2007 Muniswamy-Reddy, Holland, Braun, and Seltzer 2006 Simmhan et al. 2009 Souiah, Francalanza, and Sassone 2009 Cohen-Boulakia, Biton, Cohen, and Davidson 2008 Freire, Koop, Santos and Silva 2008 Buneman and Tan 2007 Davidson et al. Furthermore, none of the studies provide for a secure form of data provenance in Big Data applications Margo and Smogor 2010 Aggarwal 2009 Bao, Cohen-Boulakia, Davidson, Eyal, and Khanna 2009 Muniswamy-Reddy et al.
2011 Park, Ikeda, and Widom 2011 Simmhan et al.
2013 Che, Safran, and Peng 2013 Ghoshal and Plale 2013 Crawl et al. There are many studies of Hadoop or MapReduce in the area of Big Data, but only a few that discuss data provenance in Big Data or Hadoop (Chen and Plale 2015 Imran, Agrawal, Walker, and Gomes 2014 Akoush et al. Evaluations were carried out, and some evidence was found that the framework assists in the understanding and analysis of provenance data when decision-making is needed. Visionary is an application domain-free framework adapted to any system that uses the PROV provenance model. The visualization presents and highlights inferences and results obtained with the data analysis. The framework captures the provenance data and generates new information using ontologies and structural analysis of the provenance graph.
#A BETTER FINDER ATTRIBUTES ALTERNATIVE SOFTWARE#
This paper presents the Visionary framework, designed to assist in the understanding and use of provenance data through ontologies, complex network analysis, and software visualization techniques. Ontology, complex networks, and software visualization can help in this process by generating new data insights and strategic information for decision-making. However, for a better understanding and use of provenance data, efficient and user-friendly mechanisms are needed. We consider that these requirements can also be used in new application domains, such as software processes and IoT. In scientific workflows, provenance is considered essential to support experiments’ reproducibility, interpretation of results, and problem diagnosis. Provenance is recognized as a central challenge to establish the reliability and provide security in computational systems.
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