Similarity assessment and efficient retrieval of semantic workflows

Bergmann, Ralph; Gil, Yolanda

doi:10.1016/j.is.2012.07.005

Cited by 95 publications

(88 citation statements)

References 30 publications

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“…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%

“…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. This makes it hard to determine how single aspects contribute to workflow similarity, and which approach to a specific step of the comparison process provides best results.…”

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Similarity search for scientific workflows

et al. 2014

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“…These works have required the introduction of different metrics for graph comparison. Most of them are based on extensions of the graph edit distance (Minor et al, 2008;Bergmann & Gil, 2014;Kunze & Weske, 2011;Li et al, 2008;Montani et al, 2015;Dijkman et al, 2009;LaRosa et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…The works in Bergmann & Gil (2014); Montani et al (2015), on the other hand, exploit semantic information in activity comparison. In Bergmann & Gil (2014), a system working on workflows represented as semantically labeled graphs is presented. The paper proposes to use a metric in which the similarity between two mapped nodes or arcs makes explicit use of their semantic description.…”

Section: Related Workmentioning

confidence: 99%

Trace retrieval for business process operational support

Bottrighi

Canensi

Leonardi

et al. 2016

Expert Systems with Applications

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Operational support assists users while process instances are being executed, by making predictions about the instance completion, or recommending suitable actions, resources or routing decisions, on the basis of the already completed instances, stored as execution traces in the event log.In this paper, we propose a case-based retrieval approach to business process management operational support, where log traces are exploited as cases. Once past traces have been retrieved, classical statistical techniques can be applied to them, to support prediction and recommendation. The framework enables the user to submit queries able to express complex patterns exhibited by the current process instance. Such queries can be composed by several simple patterns (i.e., single actions, or direct sequences of actions), separated by delays (i.e., actions we do not care about). Delays can also be imprecise (i.e., the number of actions can be given as a range). The tool also relies on a tree structure, adopted as an index for a quick retrieval from the available event log.Our approach is highly innovative with respect to the existing literature panorama, since it is the first work that exploits case-based retrieval techniques in the operational support context; moreover, the possibility of * Corresponding author. Tel. +39 0131 360340Email addresses: alessio.bottrighi@uniupo.it (Alessio Bottrighi), canensi@di.unito.it (Luca Canensi), giorgio.leonardi@mfn.unipmn.it (Giorgio Leonardi), stefania.montani@uniupo.it (Stefania Montani), terenz@di.unito.it (Paolo Terenziani) Preprint submitted to Expert Systems with ApplicationsOctober 1, 2015 retrieving traces by querying complex patterns and the indexing strategy are major departures also with respect to other existing trace retrieval tools proposed in the Case Based Reasoning area. Thanks to its characteristics and methodological solutions, the tool implements operational support tasks in a flexible and efficient way, as demonstrated by our experimental results.

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“…Goderis and colleagues [11] apply sub-graph isomorphism techniques for finding the most similar workflows to a given one, and perform benchmarks for workflow discovery on that basis [10]. Bergman and Gil [2] go a step further, enabling users to find workflows according to different criteria (e.g., having a specific input or output type). While these approaches aim to discover workflows based on certain parameters provided by the user, in our work we aim to expose the most common fragments already in use in a workflow corpus.…”

Section: Related Workmentioning

confidence: 99%

FragFlow Automated Fragment Detection in Scientific Workflows

Garijo

Corcho

Gil

et al. 2014

2014 IEEE 10th International Conference on E-Science

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Abstract-Scientific workflows provide the means to define, execute and reproduce computational experiments. However, reusing existing workflows still poses challenges for workflow designers. Workflows are often too large and too specific to reuse in their entirety, so reuse is more likely to happen for fragments of workflows. These fragments may be identified manually by users as sub-workflows, or detected automatically. In this paper we present the FragFlow approach, which detects workflow fragments automatically by analyzing existing workflow corpora with graph mining algorithms. FragFlow detects the most common workflow fragments, links them to the original workflows and visualizes them. We evaluate our approach by comparing FragFlow results against user-defined sub-workflows from three different corpora of the LONI Pipeline system. Based on this evaluation, we discuss how automated workflow fragment detection could facilitate workflow reuse.

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Similarity assessment and efficient retrieval of semantic workflows

Cited by 95 publications

References 30 publications

Similarity search for scientific workflows

Similarity search for scientific workflows

Trace retrieval for business process operational support

FragFlow Automated Fragment Detection in Scientific Workflows

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