Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance

Leake, David; Kendall-Morwick, Joseph

doi:10.1007/978-3-540-85502-6_18

Cited by 51 publications

(57 citation statements)

References 16 publications

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“…Leake (2010) used execution traces recording provenance information to improve reasoning and explanation in CBR. In the Phala system (Leake & Kendall-Morwick, 2008), the authors supported the generation and composition of scientific workflows by mining execution traces for recommendations to aid workflow authors. Finally, Lanz et al (2010) used annotated traces recorded when a human user played video games in order to feed a case-based planner.…”

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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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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“…The LONI Pipeline has a Workflow Miner 7 module, which follows a statistical approach by measuring the components that most likely precede or are followed by a given component. Leake and Kendall-Morwick [14] rely on CBR approaches to mine provenance traces in order to suggest components when users edit new workflows. The difference with our approach in these cases is that we propose the most reused fragments as new workflows/groupings, instead of deriving the whole process network to choose the next most probable component when designing a workflow.…”

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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“…Like Margo's work, we also reduce the size and dimensionality of provenance by partitioning the graph and applying statistical post-processing. Phala [17] uses provenance information as a new experience-based knowledge source, and utilizes the information to suggest possible completion scenarios to workflow graphs. It does not, however, provide descriptive knowledge for a large provenance dataset.…”

Section: Related Workmentioning

confidence: 99%

Temporal representation for scientific data provenance

Chen

Plale

Aktaş

2012

2012 IEEE 8th International Conference on E-Science

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Abstract-Provenance of digital scientific data is an important piece of the metadata of a data object. It can however grow voluminous quickly because the granularity level of capture can be high. It can also be quite feature rich. We propose a representation of the provenance data based on logical time that reduces the feature space. Creating time and frequency domain representations of the provenance, we apply clustering, classification and association rule mining to the abstract representations to determine the usefulness of the temporal representation. We evaluate the temporal representation using an existing 10 GB database of provenance captured from a range of scientific workflows.

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Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance

Cited by 51 publications

References 16 publications

Trace retrieval for business process operational support

Trace retrieval for business process operational support

FragFlow Automated Fragment Detection in Scientific Workflows

Temporal representation for scientific data provenance

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