Tioga-2: A Direct Manipulation Database Visualization Environment
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Chimera: AVirtual Data System for Representing, Querying, and Automating Data Derivation
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Earth System Science Workbench: A Data Management Infrastructure for Earth Science Products
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Dynamic Selection of Web Services with Recommendation System
NWESP '05 Proceedings of the International Conference on Next Generation Web Services Practices
Querying and Creating Visualizations by Analogy
IEEE Transactions on Visualization and Computer Graphics
Metadata in the collaboratory for multi-scale chemical science
DCMI '03 Proceedings of the 2003 international conference on Dublin Core and metadata applications: supporting communities of discourse and practice---metadata research & applications
VisComplete: Automating Suggestions for Visualization Pipelines
IEEE Transactions on Visualization and Computer Graphics
A protocol for recording provenance in service-oriented grids
OPODIS'04 Proceedings of the 8th international conference on Principles of Distributed Systems
Graph-based workflow recommendation: on improving business process modeling
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards semantic comparison of multi-granularity process traces
Knowledge-Based Systems
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The increasingly complicated workflow systems necessitates the development of automated workflow recommendation techniques, which are able to not only speed up the workflow construction process, but also reduce the errors that are possibly made. The existing workflow recommendation systems are quite limited in that they cannot produce a correct recommendation of the next node if the upstream nodes/sub-paths that determine the occurrence of this node are not immediately connected with it. To solve this drawback, we propose in this paper a new workflow recommendation technique, called FlowRecommender. FlowRecommender features a more robust exploration capability to identify the upstream dependency patterns that are essential to the accuracy of workflow recommendation. These patterns are properly register offline to ensure a highly efficient online workflow recommendation. The experimental results confirm the promising effectiveness and efficiency of FlowRecommender.