A Multi-Tier Provenance Model for Global Climate Research

Abstract

Global climate researchers rely upon many forms of sensor data and analytical methods to help profile subtle changes in climate conditions. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program provides researchers with curated Value Added Products (VAPs) resulting from continuous instrumentation streams, data fusion, and analytical profiling. The ARM operational staff and software development teams (data producers) rely upon a number of techniques to ensure strict quality control (QC) and quality assurance (QA) standards are maintained. Climate researchers (data consumers) are highly interested in obtaining as much provenance evidence as possible to establish data trustworthiness. Currently all the evidence is not easily attainable or identifiable without significant efforts to extract and piece together information from configuration files, log files, codes, or status information on the ARM website. Our objective is to identify a provenance model that serves the needs of both the VAP producers and consumers. This paper shares our initial results -a comprehensive multi-tier provenance model. We describe how both ARM operations staff and the climate research community can greatly benefit from this approach to more effectively assess and quantify the data historical record.

Leveraging the Open Provenance Model as a Multi-tier Model for Global Climate Research

“…We split out a special category, developers, which are part of operations and who have a special need for provenance related to debugging activities [5].…”

“…Because of diverse provenance-related questions being asked at different granularities, we also needed a flexible model that could be dissected in a variety of ways to support a number of analytical mash-ups. Based on all these factors a multi-scale conceptual model resulted [5].…”