Ontology Mapping in Semantic Time Series Processing and Climate Change Prediction

In today’s time series processing there is more and more a need for addressing diverse user groups interested in a specific domain with appropriate user tailored time series data. The complexity of time series (e.g. involved data from different data sources and/or domains, visualization and representation, etc.) is growing rapidly. As a consequence, it means that users need to find a path through the jungle of time series data. After we have presented our concepts for semantic time series filtering and enrichment of time series with meta-information and annotations (Božić et al., 2012), we are now going to present a validation of these methods in the specific use case of climate change prediction. In this specific use case also called validation scenario, we demonstrate this approach in the Climate Twins application, which is a prediction model for geo-regions based on temperature and precipitation. The use case shows the how to select the right time series data and how to provide the right resources to the right user groups. In order to reach this goal, the idea has been to produce a domain specific ontology for a dedicated user group and to use it for the definition of basic discovery criteria of environmental respectively climate change related resources. The validation has been performed during the TaToo project, where a show case been developed to demonstrate the advantages of semantic enrichment for climate change prediction data using the Climate Twins application.