Domain-specific summarization of Life-Science e-experiments from provenance traces

Abstract

Translational research in Life-Science nowadays leverages e-Science platforms to analyse and produce huge amounts of data. With the unprecedented growth of Life-Science data repositories, identifying relevant data for analysis becomes increasingly difficult. The instrumentation of e-Science platforms with provenance tracking techniques provide useful information from a data analysis process design or debugging perspective. However raw provenance traces are too massive and too generic to facilitate the scientific interpretation of data. In this paper, we propose an integrated approach in which Life-Science knowledge is (i) captured through domain ontologies and linked to Life-Science data analysis tools, and (ii) propagated through rules to produced data, in order to constitute human-tractable experiment summaries. Our approach has been implemented in the Virtual Imaging Platform (VIP) and experimental results show the feasibility of producing few domain-specific statements which opens new data sharing and repurposing opportunities in line with Linked Data initiatives.

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

“…Levels 0–2 are generic and domain-neutral and can apply to any scientific workflow. However, domain-specific information/metadata about data and processes plays an important role in better understanding of the analysis and exploitation of provenance information, e.g., for meaningful queries to extract information to the domain under consideration [73,74]. The addition of domain-specific metadata, e.g., file formats, user-defined tags, and other annotations to generic retrospective provenance can improve the white-boxness by providing domain context to the analysis as described in R6-annotations .…”

Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

“…They apply reduction rules to summarize the workflow specification by abstracting away steps that are not relevant from the scientific point-of-view. Other approaches have been proposed in the same spirit, such as [68]. While these three approaches have all the same goal, they use very different techniques, from graph algorithms to web semantics, demonstrating the wide spectrum of research domains that can be used to tackle the problem of workflow reduction.…”

“…First-of-all, in the previous section, approaches such as [66,67,68] have been mentioned as possibilities to reduce the complexity of workflows and make them easier to understand, allowing to hide over technical parts of workflows. Other approaches either reduce the (structural) complexity of workflows, by detecting fragments to be modified such as DistillFlow [73](dedicated to Taverna) or have mined workflow fragments to be reused [74].…”

Using a provenance model and spatiotemporal information to integrate heterogeneous biodiversity semantic data.

“…It provided basic provenance information: the sources queried, the number of triples returned and the retrieval time. Gaignard et al (GAIGNARD et al, 2014) proposed an integrated provenance approach in which Life-Science knowledge is captured through domain ontologies and linked to Life-Science data analysis tools. Until now, the focus in previous work has been to extend the DCMI and OPM provenance models with specific goals (e.g.…”