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Incorporating semantics in pattern-based scientific workflow recommender systems

Soomro, Kamran; Munir, Kamran; McClatchey, Richard

Authors

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Kamran Munir Kamran2.Munir@uwe.ac.uk
Associate Professor in Data Science



Abstract

Recommender systems are used to enable decision support. Using them to assist users when designing scientific workflows introduces a number of challenges. These include selecting appropriate components and specifying correct parameter values. Pattern-based workflow recommender systems employ historical usage patterns to generate recommendations. Such systems can intelligently adapt with use. Semantics, on the other hand, can enable recommender systems to intelligently infer new relationships between workflow components. Combining both approaches can help to overcome the drawbacks of each approach and improve the accuracy of the suggestions. To this end, a framework for a hybrid workflow design recommender system is presented in this paper along with the accompanying suggestion generation algorithm. An illustrative example is also presented to demonstrate how the system helps in constructing a workflow. The performance of the framework is compared with an existing pattern-based system using a dataset of neuroimaging workflows. The evaluation results demonstrate that the proposed system outperforms the existing system in a number of different scenarios. The improvement in the performance of the proposed system enhances the usability of the system for users and allows
them to more efficiently construct workflows.

Citation

Soomro, K., Munir, K., & McClatchey, R. (2015, July). Incorporating semantics in pattern-based scientific workflow recommender systems. Paper presented at IEEE Science and Information Conference 2015

Presentation Conference Type Conference Paper (unpublished)
Conference Name IEEE Science and Information Conference 2015
Start Date Jul 28, 2015
End Date Jul 30, 2015
Publication Date Jul 1, 2015
Peer Reviewed Not Peer Reviewed
Pages 565-571
Keywords workflow design, recommender systems, workflow execution systems, ontologies
Publisher URL http://dx.doi.org/10.1109/SAI.2015.7237199
Additional Information Additional Information : © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Title of Conference or Conference Proceedings : IEEE Science and Information Conference 2015

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