Work2013

ProvenanceCurious

Mohammad Rezwanul Huq · Peter M. G. Apers · Andreas Wombacher

21 citations0/0 files storedInternational Conference on Extending Database Technology (EDBT)

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Scientific Computing and Data Management Research Data Management Practices Environmental Monitoring and Data Management

Abstract

The increasing data volume and highly complex models used in different domains make it difficult to debug models in cases of anomalies. Data provenance provides scientists sufficient information to investigate their models. In this paper, we propose a tool which can infer fine-grained data provenance based on a given script. The tool is demonstrated using a hydrological model. The tool is also tested successfully handling other scripts in different contexts.

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2016Linking Prospective and Retrospective Provenance in Scripts 2019Towards integration of data-driven agronomic experiments with data provenance 2018Provenance Inference Techniques: Taxonomy, comparative analysis and design challenges

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