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Distributed data provenance for large-scale data-intensive computing
2013
2013 IEEE International Conference on Cluster Computing (CLUSTER)
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It has become increasingly important to capture and understand the origins and derivation of data (its provenance). A key issue in evaluating the feasibility of data provenance is its performance, overheads, and scalability. In this paper, we explore the feasibility of a general metadata storage and management layer for parallel file systems, in which metadata includes both file operations and provenance metadata. We experimentally investi
doi:10.1109/cluster.2013.6702685
dblp:conf/cluster/ZhaoSMR13
fatcat:yxktbz7zu5gf7m2umseien36de