A process mining-based error correction approach to improve data quality of an IoT-sourced event log

TL;DR

This paper proposes a process mining-based error correction method to enhance data quality in IoT event logs, addressing data errors that compromise analysis accuracy in smart home systems.

Contribution

It introduces a novel error correction approach leveraging process mining techniques specifically tailored for IoT-sourced event logs.

Findings

Improved data accuracy in IoT event logs

Enhanced reliability of data analysis results

Effective error detection and correction in smart home data

Abstract

Internet of Things (IoT) systems are vulnerable to data collection errors and these errors can significantly degrade the quality of collected data, 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 and considering the effectiveness of error correction methods for a smart home use case.