Towards a Quality Model for Effective Data Selection in Collaboratories Export

by: Y. L. Simmhan, B. Plale, D. Gannon

Data Engineering Workshops, 2006. Proceedings. 22nd International Conference on (2006), pp. 72-72.

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Abstract

Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data.

@inproceedings{citeulike:790400, abstract = {Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data.}, author = {Simmhan, Y. L. and Plale, B. and Gannon, D.}, citeulike-article-id = {790400}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1623867}, journal = {Data Engineering Workshops, 2006. Proceedings. 22nd International Conference on}, keywords = {collaboratories, data, model, quality, selection}, pages = {72}, posted-at = {2008-01-13 21:50:59}, priority = {2}, title = {Towards a Quality Model for Effective Data Selection in Collaboratories}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1623867}, year = {2006} }

RIS record

TY - CONF ID - citeulike:790400 L3 - citeulike-article-id:790400 N2 - Data-driven scientific applications utilize workflow frameworks to execute complex dataflows, resulting in derived data products of unknown quality. We discuss our on-going research on a quality model that provides users with an integrated estimate of the data quality that is tuned to their application needs and is available as a numerical quality score that enables uniform comparison of datasets, providing a way for the community to trust derived data. JF - Data Engineering Workshops, 2006. Proceedings. 22nd International Conference on EP - 72 TI - Towards a Quality Model for Effective Data Selection in Collaboratories SP - 72 KW - collaboratories KW - data KW - model KW - quality KW - selection AU - Simmhan, YL AU - Plale, B AU - Gannon, D PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1623867 ER -

Towards a Quality Model for Effective Data Selection in Collaboratories Export

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