Finding Root Causes for Outliers in Semantically Annotated Sensor Data

Strobel T, Pychynski T, Harth A (2024)

Publication Type: Conference contribution

Publication year: 2024

URI: https://2024.eswc-conferences.org/wp-content/uploads/2024/04/ESWC_2024_paper_339.pdf

Open Access Link: https://2024.eswc-conferences.org/wp-content/uploads/2024/04/ESWC_2024_paper_339.pdf

Abstract

Causal inference creates insights into observational data. Such insights could explain an outlying value to perform Root Cause Analysis. But how can causal inference be used with semantically annotated observations? The following demo showcases how to use semantically annotated sensor data for causal inference. The method’s implementation uses an agent pattern interacting with a knowledge graph.

Authors with CRIS profile

Tim Strobel Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Andreas Harth Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme

How to cite

APA:

Strobel, T., Pychynski, T., & Harth, A. (2024). Finding Root Causes for Outliers in Semantically Annotated Sensor Data. In Proceedings of the Extended Semantic Web Conference.

MLA:

Strobel, Tim, Tim Pychynski, and Andreas Harth. "Finding Root Causes for Outliers in Semantically Annotated Sensor Data." Proceedings of the Extended Semantic Web Conference 2024.

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