Workflow specification, in science as in business, can be a difficult task, since it requires a deep knowledge of the domain to be able to model the chaining of the steps that compose the process of interest, as well as awareness of the computational tools, e.g., services, that can be utilized to enact such steps. To assist designers in this task, we investigate in this paper a methodology that consists in exploiting existing workflow specifications that are stored and shared in repositories, to identify workflow fragments that can be re-utilized and re-purposed by designers when specifying new workflows. Specifically, we present a method for identifying fragments that are frequently used across workflows in existing repositories, and therefore are likely to incarnate patterns that can be reused in new workflows. We present a keyword-based search method for identifying the fragments that are relevant for the needs of a given workflow designer. We go on to present an algorithm for composing the retrieved fragments with the initial (incomplete) workflow that the user designed, based on compatibility rules that we identified, and showcase how the algorithm operates using an example from eScience.

Public repositories of scientific and business workflows are gaining growing attention as a means to enable understanding, reuse and ultimately the reproducibility of the processes such workflows incarnate. However, as the number of workflows hosted by such repositories grows, their users face difficulties when it come to exploring and querying workflows. In this paper, we explore a functionality that can help repository administrators to index their workflows, and users to identify the workflows that are of interest to them. In particular, we investigate the problem of finding frequent and similar fragments in workflows using graph mining techniques. Our objective is not to come up with yet another graph mining or similarity technique. Instead, we explore different representations that can be used for encoding workflows before assessing their similarity taking into consideration the effectiveness and efficiency of the mining algorithm.