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ThinkMind // DBKDA 2020, The Twelfth International Conference on Advances in Databases, Knowledge, and Data Applications // View article dbkda_2020_1_70_50032
Tackling Semantic Shift in Industrial Streaming Data Over Time
 Authors:
Lisa Ehrlinger
Christian Lettner
Johannes Himmelbauer Keywords: Semantic Shift; Streaming Data; Data Quality; Process Stability Monitoring; Data Preprocessing Abstract:
Industrial production processes generate huge amounts of streaming data, usually collected by the deployed machines. To allow the analysis of this data (e.g., for process stability monitoring or predictive maintenance), it is necessary that the data streams are of high quality and comparable between machines. A common problem in such scenarios is "semantic shift". For example, a sensor's weight unit might shift from tons to kilograms after a firmware update and still store the collected values to the same variable. In this paper, we discuss semantic shift theoretically and by means of an industrial case study from a production plant in Austria, where several hundred injection molding machines are employed. The data collected by these machines is used to monitor the stability of the production process with machine learning algorithms. In the following, we present and discuss the data preprocessing system we developed for the production plant to handle semantic shift for huge amounts of streaming data. Pages: 36 to 39 Copyright: Copyright (c) IARIA, 2020 Publication date: September 27, 2020 Published in: conference ISSN: 2308-4332 ISBN: 978-1-61208-790-0 Location: Lisbon, Portugal Dates: from September 27, 2020 to October 1, 2020
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