Basic and personalized pattern-based workflow fragments discovery

Abstract

With an increasing number of scientific workflows accessible on public repositories, the mechanism for discovering and recommending workflow fragments is important to facilitate the reuse and repurposing of legacy workflows when novel workflows are to be constructed, where dependencies among workflow activities, which implies functional patterns with different types and characteristics, are specified in the specification of workflows. Traditional approaches ignore or seldom consider this aspect, which may have certain influence on the quality of personalized recommendation. To address this challenge, this paper proposes a novel workflow fragment discovery mechanism for personalized requirements, where discovery strategies of basic and personalized patterns are presented independently. Specifically, frequent basic subfunctions are discovered from scientific workflows by applying the frequent subgraph mining algorithm. Similar subfunctions are clustered considering their semantic relevance of topics, and clusters with high functional frequency are assumed as basic patterns. Thereafter, the multi-dimensional representation of scientific workflows is constructed to explore workflow relevance. Workflow clustering is conducted, and frequent personalized functions are discovered from clusters with similar workflows and assumed as personalized patterns under respective contents. For a personalized requirement given in terms of a workflow template, target basic patterns and candidate subfunctions are discovered, and they compose the backbone structure of solution in a novel coverage strategy. Candidate personalized patterns are applied to cover remaining functionalities of requirement. An optimal solution is obtained through atomic service optimization. Evaluation results show that this technique is accurate on discovering fragment solutions for personalized requirements in comparison with the state-of-the-art techniques.