Over the last years, scientific workflows have become mature enough to be used in a production style. However, despite the increasing maturity, there is still a shortage of tools for searching, adapting, and reusing workflows that hinders a more generalized adoption by the scientific communities. Indeed, due to the limited availability of machine-readable scientific metadata and the heterogeneity of workflow specification formats and representations, new ways to leverage alternative sources of information that complement existing approaches are needed. In this paper we address such limitations by applying statistically enriched generalized trie structures to exploit workflow execution provenance information in order to assist the analysis, indexing and search of scientific workflows. Our method bridges the gap between the description of what a workflow is supposed to do according to its specification and related metadata and what it actually does as recorded in its provenance execution trace. In doing so, we also prove that the proposed method outperforms SPARQL 1.1 Property Paths for querying provenance graphs.
Alternatively, we consider a workflow as a set of linked processes that can be analyzed in order to obtain a deeper knowledge of its execution, behavior and objectives. There are some ongoing research initiatives that follow this approach, in an attempt to extract patterns by automatically learning from the interlinked structures. @cite_7 the authors refer to this process as link pattern discovery being its main goal the characterization of interlinked relations which may or may not be defined explicitly. Also, at @cite_4 @cite_23 the authors referred to the term link mining in order to indicate how data mining techniques can explicitly consider the links between objects when building predictive or descriptive models of linked data. Notice that the analysis of interlinked structures are also applicable to other relevant open datasets such as DBpedia http: dbpedia.org .
@cite_23 @cite_4 @cite_7
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A key challenge for data mining is tackling the problem of mining richly structured datasets, where the objects are linked in some way. Links among the objects may demonstrate certain patterns, which can be helpful for many data mining tasks and are usually hard to capture with traditional statistical models. Recently there has been a surge of interest in this area, fueled largely by interest in web and hypertext mining, but also by interest in mining social networks, security and law enforcement data, bibliographic citations and epidemiological records. As the explosive growth of online linked data, an emerging problem is what and how we can learn from these data. An important knowledge we can obtain is the link patterns among objects, which are helpful for characterizing, analyzing and understanding of linked data. In this paper, we present a novel approach of mining link patterns. A Typed Object Graph is proposed as the data model, and a gSpan-based algorithm is proposed for pattern mining. A type determination policy is introduced in cases of multi-types and a data clustering algorithm is proposed to improve scalability. Time performance and mining results are discussed by experiments.