Start Date

26-6-2018 3:40 PM

End Date

26-6-2018 5:00 PM

Abstract

This work demonstrates a template framework for acquiring and integrating heterogeneous environmental timeseries. The Internet of Things contributes towards the high availability of environmental timeseries datasets. These are available through different protocols and stored under diverse, custom formats, rendering data acquisition and integration a laborious step of the environmental data lifecycle. We designed and implemented a template framework called EDAM to facilitate data acquisition and integration. EDAM is founded on re-usable templates (i.e. an abstract representation of a data file's structure written using programming language agnostic semantics), and requires no strong computer science background. EDAM supports for customized data dissemination, as instructed by output templates. We demonstrate EDAM generality in seven case studies, by scraping online meteorological data, extracting observations from a relational database, and aggregating historical timeseries stored in local files.

Stream and Session

A1: Towards More Interoperable, Reusable and Scalable Environmental Software

or

C15: Does Data and Science Make a Difference for Better Decisions in Environmental Management?

putting it in c15 -dpa

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Jun 26th, 3:40 PM Jun 26th, 5:00 PM

A template framework for environmental timeseries data acquisition

This work demonstrates a template framework for acquiring and integrating heterogeneous environmental timeseries. The Internet of Things contributes towards the high availability of environmental timeseries datasets. These are available through different protocols and stored under diverse, custom formats, rendering data acquisition and integration a laborious step of the environmental data lifecycle. We designed and implemented a template framework called EDAM to facilitate data acquisition and integration. EDAM is founded on re-usable templates (i.e. an abstract representation of a data file's structure written using programming language agnostic semantics), and requires no strong computer science background. EDAM supports for customized data dissemination, as instructed by output templates. We demonstrate EDAM generality in seven case studies, by scraping online meteorological data, extracting observations from a relational database, and aggregating historical timeseries stored in local files.