Start Date

25-6-2018 3:40 PM

End Date

25-6-2018 5:20 PM

Abstract

Environmental timeseries acqusition, integration and transformation into a consistent data format is becoming more and more challenging in the Internet of Things era, but also for managing legacy model data files. To date, data transformation from diverse sources into one data format requires significant efforts to tackle semantic heterogeneity. In this work we present a declarative approach for environmental timeseries data transformation using semantics. We use a template to annotate environmental data files with terms from a vocabulary. We demonstrate how a reasoner may be employed to resolve synonyms across different vocabularies. This enables to annotate each data file once; and transform its contents using templates with other vocabularies without needing to re-annotate it. We developed a case study where we transform meteorological input files of four agricultural models. With our approach, a certain data file format can be represented through a single template, and by assigning synonym terms we enable automatic transformation into other formats. This facilitates environmental timeseries transformation overcoming semantic heterogeneity, while lowering the e-science barriers.

Stream and Session

F5: New and Improved Methods in Agricultural Systems Modelling

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Jun 25th, 3:40 PM Jun 25th, 5:20 PM

Towards a semantic approach for environmental timeseries data fusion

Environmental timeseries acqusition, integration and transformation into a consistent data format is becoming more and more challenging in the Internet of Things era, but also for managing legacy model data files. To date, data transformation from diverse sources into one data format requires significant efforts to tackle semantic heterogeneity. In this work we present a declarative approach for environmental timeseries data transformation using semantics. We use a template to annotate environmental data files with terms from a vocabulary. We demonstrate how a reasoner may be employed to resolve synonyms across different vocabularies. This enables to annotate each data file once; and transform its contents using templates with other vocabularies without needing to re-annotate it. We developed a case study where we transform meteorological input files of four agricultural models. With our approach, a certain data file format can be represented through a single template, and by assigning synonym terms we enable automatic transformation into other formats. This facilitates environmental timeseries transformation overcoming semantic heterogeneity, while lowering the e-science barriers.