A showcase of semantic time series processing

Abstract

PurposeThe purpose of this paper is to present a showcase of semantic time series processing which demonstrates how this technology can improve time series processing and community building by the use of a dedicated language.Design/methodology/approachThe authors have developed a new semantic time series processing language and prepared showcases to demonstrate its functionality. The assumption is an environmental setting with data measurements from different sensors to be distributed to different groups of interest. The data are represented as time series for water and air quality, while the user groups are, among others, the environmental agency, companies from the industrial sector and legal authorities.FindingsA language for time series processing and several tools to enrich the time series with meta‐data and for community building have been implemented in Python and Java. Also a GUI for demonstration purposes has been developed in PyQt4. In addition, an ontology for validation has been designed and a knowledge base for data storage and inference was set up. Some important features are: dynamic integration of ontologies, time series annotation, and semantic filtering.Research limitations/implicationsThis paper focuses on the showcases of time series semantic language (TSSL), but also covers technical aspects and user interface issues. The authors are planning to develop TSSL further and evaluate it within further research projects and validation scenarios.Practical implicationsThe research has a high practical impact on time series processing and provides new data sources for semantic web applications. It can also be used in social web platforms (especially for researchers) to provide a time series centric tagging and processing framework.Originality/valueThe paper presents an extended version of the paper presented at iiWAS2012.