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, and increases
community’s trust in derived data.
%0 Conference Paper
%1 Simmhan:sciflow:2006
%A Simmhan, Yogesh L.
%A Plale, Beth
%A Gannon, Dennis
%B Workshop on Workflow and Data Flow for Scientific Applications (SciFlow)
%D 2006
%I IEEE
%K escience, iu, karma, paper, peer provenance, reviewed short workflows,
%P 1--4
%R 10.1109/ICDEW.2006.150
%T Towards a Quality Model for Effective Data Selection in Collaboratories
%X 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, and increases
community’s trust in derived data.
@inproceedings{Simmhan:sciflow:2006,
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, and increases
community’s trust in derived data.},
added-at = {2014-08-13T04:08:36.000+0200},
author = {Simmhan, Yogesh L. and Plale, Beth and Gannon, Dennis},
biburl = {https://www.bibsonomy.org/bibtex/2034d90a87c930386a1f60615b005c927/simmhan},
booktitle = {Workshop on Workflow and Data Flow for Scientific Applications (SciFlow)},
doi = {10.1109/ICDEW.2006.150},
interhash = {02604fc35d50e0a524c6d07bf3a17bd0},
intrahash = {034d90a87c930386a1f60615b005c927},
keywords = {escience, iu, karma, paper, peer provenance, reviewed short workflows,},
month = {April},
owner = {Simmhan},
pages = {1--4},
publisher = {IEEE},
series = {International Conference on Data Engineering Workshops},
timestamp = {2014-08-13T04:08:36.000+0200},
title = {Towards a Quality Model for Effective Data Selection in Collaboratories},
year = 2006
}