Improving workflow design by mining reusable tasks

Abstract

Background: With the increasing popularity of scientific workflow management systems (SWfMS), more and more workflow specifications are becoming available. Such specifications contain precious knowledge that can be reused to produce new workflows. It is a fact that provenance data can help reusing third party code. However, finding the dependencies among programs without the support of a tool is not a trivial activity and, in many cases, becomes a barrier to build more sophisticated models and analysis. Due to the huge number of task versions available and their configuration parameters, this activity is highly error prone and counterproductive. Methods: In this work, we propose workflow recommender (WR), a recommendation service that aims at suggesting frequent combinations of workflow tasks for reuse. It works similarly to an e-commerce application that applies data mining techniques to help users find items they would like to purchase, predicting a list based on other user's choices. Results: Our experiments show that our approach is effective both in terms of performance and precision of the results. Conclusions:The approach is general in the sense that it can be coupled to any SWfMS.

Cross-domain graph based similarity measurement of workflows

“…So, it uses ISA-Tab handler for scientific workflows that allows the linkage of a single sample to multiple analyses employing various assays [14]. For other workflows in other domains a XML-Based workflow Event Logging Mechanism has been used in XES format [39].…”

“…Recently, Workflow Recommenders (WRs) [14] in PAISs have proposed recommendation services that aim at suggesting frequent combinations of workflow tasks for reuse. Some of these recommenders apply data mining techniques such as similarity measurement to help users find items to improve their workflow designs by prediction [14,15].…”

“…Recently, Workflow Recommenders (WRs) [14] in PAISs have proposed recommendation services that aim at suggesting frequent combinations of workflow tasks for reuse. Some of these recommenders apply data mining techniques such as similarity measurement to help users find items to improve their workflow designs by prediction [14,15]. For instance Wang et al [15] has proposed and evaluated a design space of four different WR algorithms based on data mining, which can be used to recommend new workflows and their associated videos to software users.…”

“…Besides, the authors in [15] did not evaluate their proposed algorithms with different datasets of different domains. Tosta et al [14], as another example proposed a WR method that works potentially in different domains. However, they just focused on scientific workflow domains in practice.…”

Assessing the effectiveness of automated service composition

“…In both [19] and [21], the myExperiment repository was used to gather information on how to combine services, as we did in this work, but with focus on service recommendations. Also for scientific workflows, [33] proposes a recommendation service that uses sequence mining, a data mining technique for finding frequent sequential events in a dataset. Our paper, on the other hand, focuses on the challenge of automated service composition based on typical relationships between services, such as the compatibility between input and output parameters.…”