Abstract
Although Internet of Things (IoT) systems are widely used in various industries, they are prone to data collection errors due to device limitations and environmental factors. These errors can significantly degrade the quality of collected data and the event log extracted from raw sensor readings, impact data analysis and lead to inaccurate or distorted results. This article emphasizes the importance of evaluating data quality and errors before proceeding with analysis. The effectiveness of three error correction methods, a rule-based method and a Process Mining (PM)-based method which are adjusted for a smart home use case, and their combination was also investigated in resolving log errors. The study found that understanding different types and sources of errors, and adapting the error correction algorithm based on this knowledge of error sources, can greatly improve the algorithm’s efficiency in addressing various error types.
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