Abstract
This paper introduces the concept of a knowledge graph for time series data, which allows for a structured management and propagation of characteristic time series information and the ability to support query-driven data analyses. We gradually link and enrich knowledge obtained by domain experts or previously performed analyses by representing globally and locally occurring time series insights as individual graph nodes. Supported by a utilization of techniques from automated knowledge discovery and machine learning, a recursive integration of analytical query results is exploited to generate a spectral representation of linked and successively condensed information. Besides a time series to graph mapping, we provide an ontology describing a classification of maintained knowledge and affiliated analysis methods for knowledge generation. After a discussion on gradual knowledge enrichment, we finally illustrate the concept of knowledge propagation based on an application of state-of-the-art methods for time series analysis.
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Graß, A., Beecks, C., Chala, S.A., Lange, C., Decker, S. (2023). A Knowledge Graph for Query-Induced Analyses of Hierarchically Structured Time Series Information. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_16
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