Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Fast computing reachability labelings for large graphs with high compression rate
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Finding reliable subgraphs from large probabilistic graphs
Data Mining and Knowledge Discovery
Optimizing user views for workflows
Proceedings of the 12th International Conference on Database Theory
Querying and Managing Provenance through User Views in Scientific Workflows
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Detecting and resolving unsound workflow views for correct provenance analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Mining frequent itemsets from uncertain data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Computing label-constraint reachability in graph databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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The view of data provenance provides an approach of data abstraction and encapsulation by partitioning tasks in the data provenance graph (DPG) of scientific workflow into a set of composite modules due to the data flow relations among them, so as to efficiently decrease the workload consumed by researchers making analysis on the data provenance and the time needed in doing data querying. However, unless a view is carefully designed, it may not preserve the dataflow between tasks in the workflow. Concentrating on this scenario, we propose a method for reconstructing unsound view. We also design a polynomial-time algorithm, and analyze its maximal time complexity. Finally, we give an example and conduct comprehensive experiments to show the feasibility and effectiveness of our method.