Abstract
As workflow systems get more widely used, the number of workflows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of workflow and provenance graphs. We propose two different representations for these graphs and present an experimental evaluation, using a collection of 1,700 workflow graphs, where we study the trade-offs of these representations and the effectiveness of alternative clustering techniques.
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CITATION STYLE
Santos, E., Lins, L., Ahrens, J. P., Freire, J., & Silva, C. T. (2008). A first study on clustering collections of workflow graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5272, pp. 160–173). Springer Verlag. https://doi.org/10.1007/978-3-540-89965-5_18
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