Characterizing quality of resilience in scientific workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
Distributed data mining patterns and services: an architecture and experiments
Concurrency and Computation: Practice & Experience
Analysing Quality of Resilience in Fish4Knowledge Video Analysis Workflows
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Weka4WS is an extension of the Weka toolkit to support remote execution of data mining tasks as Grid services. A first version of Weka4WS supporting concurrent execution of multiple data mining tasks on remote Grid nodes has been presented in a previous work. In this paper we present a new version supporting also the composition and execution of data mining workflows on a Grid. This new version of Weka4WS extends the KnowledgeFlow component of Weka by allowing the data mining tasks of the workflow to run in parallel on different machines, hence reducing the execution time. Besides the performance improvement, the capability of designing data mining applications as workflows allows to define typical patterns and to reuse them in different contexts. In this paper we describe the architecture of the system, the functionalities of the Weka4WS KnowledgeFlow, and some examples of use with their performance.