Provenance Tracking in an Earth Science Data Processing System

Tilmes, Curt; Fleig, A. J.

doi:10.1007/978-3-540-89965-5_23

Cited by 16 publications

(9 citation statements)

References 4 publications

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“…A recent Principles of Provenance Workshop [4] discussed the difficulty of putting provenance into practice. NASA shared [5] their continual challenges collecting, assessing, and disseminating satellite remote sensing data, and explored the implications of determining which provenance to track to accomplish specific research goals. In March 2009, a NASA technical note [6] was published underscoring the need for provenance to be coupled with the scientific record and the need for further standardization in disseminated NetCDF files.…”

Section: Relevant Researchmentioning

confidence: 99%

A Multi-Tier Provenance Model for Global Climate Research

Stephan

Halter

Gibson

et al. 2009

2009 International Conference on Network-Based Information Systems

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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.

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Section: Relevant Researchmentioning

confidence: 99%

A Multi-Tier Provenance Model for Global Climate Research

Stephan

Halter

Gibson

et al. 2009

2009 International Conference on Network-Based Information Systems

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“…In the 1990s, a conceptual design of GIS with the capability to record provenance was proposed to document data sources, data transformations and intermediate and final data products [33]. Since then, a number of methods to capture provenance have been proposed in the geospatial domain [34][35][36][37][38][39], but little research has been done in the context of a service-oriented environment. In contrast to the desktop environment, a service-oriented architecture combines distributed data and loosely coupled web services to conduct scientific study or to provide decision support [32,40].…”

Section: Literaturementioning

confidence: 99%

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Goodchild

et al. 2013

IJGI

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Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

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“…The process documentation includes the algorithms used, their versions, the original source code, a complete description of the processing environment and even the algorithm design documents themselves. Essentially, provenance enables a virtual archive of satellite data, which otherwise would be lost or difficult to recreate [84].…”

Section: Provenance In Geospatial Applicationsmentioning

confidence: 99%

Metadata and Provenance Management

Deelman¹,

Berriman²,

Chervenak³

et al. 2009

Scientific Data Management

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Scientists today collect, analyze, and generate TeraBytes and PetaBytes of data. These data are often shared and further processed and analyzed among collaborators. In order to facilitate sharing and data interpretations, data need to carry with it metadata about how the data was collected or generated, and provenance information about how the data was processed. This chapter describes metadata and provenance in the context of the data lifecycle. It also gives an overview of the approaches to metadata and provenance management, followed by examples of how applications use metadata and provenance in their scientific processes.

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Provenance Tracking in an Earth Science Data Processing System

Cited by 16 publications

References 4 publications

A Multi-Tier Provenance Model for Global Climate Research

A Multi-Tier Provenance Model for Global Climate Research

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Metadata and Provenance Management

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