Similarity-based workflow clustering

Silva, V ́ itor; Chirigati, Fernando; Maia, Kely; Ogasawara, Eduardo; Oliveira, Daniel de; Braganholo, Vanessa; Murta, Leonardo; Mattoso, Marta

doi:10.6062/jcis.2011.02.01.0029

Cited by 15 publications

(24 citation statements)

References 19 publications

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“…Another option is to compare labels by their Levenshtein edit distance [4]. To take full advantage of the information contained in a modules specification, however, it seems advantageous to compute module similarity based on a variety of attributes associated with each module, as done in [34]. Which attributes are present in a given module largely depends on the type of operation it provides.…”

Section: Pairwise Module Comparisonmentioning

confidence: 99%

“…When module comparison is based on more complex similarity assessment, the best similarity match between the modules of the two compared workflows has to be found explicitly. Previous approaches to this task include greedy selection of mapped modules [34] and computation of the mapping of maximum overall weight [4], both of which have been included in our framework. For clarity of presentation, in the following we only refer to the latter approach of maximum weight matching (mw).…”

Section: Module Mappingmentioning

confidence: 99%

“…pw3 compares single attributes in the same way as pw0 but uses higher, but not uniform weights for labels, script and service uri, followed by service name and service authority in the order listed, similar to [34].…”

Section: Pairwise Module Comparisonmentioning

confidence: 99%

“…There have been numerous studies investigating both annotational [11,36,17] and structural [34,33,36,4,18,17,38] approaches, but their comparison is hindered by a number of factors. Firstly, the process of scientific workflow comparison entails several steps from comparison of single modules to comparison of whole workflows [35,4] -each of which may be treated differently in the methods considered.…”

Section: Introductionmentioning

confidence: 99%

“…Challenges include the detection of functionally equivalent workflows, grouping of work-flows into functional clusters, workflow retrieval, or the use of existing workflows in the design of novel workflows [36,34,33,4,18]. The core operation necessary for meeting any of these challenges is the algorithmic comparison of two workflows regarding their functional similarity.…”

Section: Introductionmentioning

confidence: 99%

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Similarity search for scientific workflows

et al. 2014

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With the increasing popularity of scientific workflows, public repositories are gaining importance as a means to share, find, and reuse such workflows. As the sizes of these repositories grow, methods to compare the scientific workflows stored in them become a necessity, for instance, to allow duplicate detection or similarity search. Scientific workflows are complex objects, and their comparison entails a number of distinct steps from comparing atomic elements to comparison of the workflows as a whole. Various studies have implemented methods for scientific workflow comparison and came up with often contradicting conclusions upon which algorithms work best. Comparing these results is cumbersome, as the original studies mixed different approaches for different steps and used different evaluation data and metrics. We contribute to the field (i) by disecting each previous approach into an explicitly defined and comparable set of subtasks, (ii) by comparing in isolation different approaches taken at each step of scientific workflow comparison, reporting on an number of unexpected findings, (iii) by investigating how these can best be combined into aggregated measures, and (iv) by making available a gold standard of over 2000 similarity ratings contributed by 15 workflow experts on a corpus of almost 1500 workflows and re-implementations of all methods we evaluated.

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Section: Pairwise Module Comparisonmentioning

confidence: 99%

Section: Module Mappingmentioning

confidence: 99%

Section: Pairwise Module Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Similarity search for scientific workflows

et al. 2014

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MANSOR: A module alignment method based on neighbor information for scientific workflow

Cao

Qian

et al. 2023

Concurrency and Computation

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SummaryFinding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

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