Describing Data Processing Pipelines in Scientific Publications for Big Data Injection

作者: Sepideh Mesbah , Alessandro Bozzon , Christoph Lofi , Geert-Jan Houben

DOI: 10.1145/3057148.3057149

关键词:

摘要: The rise of Big Data analytics has been a disruptive game changer for many application domains, allowing the integration into domain-specific applications and systems insights knowledge extracted from external big data sets. effective "injection" demands an understanding properties available sets, expertise on most suitable methods collection, enrichment analysis. A prominent source is scientific literature, where processing pipelines are described, discussed, evaluated. Such however not readily accessible, due to its distributed unstructured nature. In this paper, we propose novel ontology aimed at modeling pipelines, their related artifacts, as described in publications. result requirement analysis that involved experts both academia industry. We showcase effectiveness our by manually applying it collection publications describing methods.

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