An Interactive Error-correcting Approach for IoT-sourced Event Logs

Mohsen Shirali, Zahra Ahmadi, Carlos Fernández-Llatas, José Luis Bayo Montón, Gemma Di Federico

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
Article number21
JournalACM Transactions on Internet of Things
Volume5
Issue number4
Number of pages30
ISSN2577-6207
DOIs
Publication statusPublished - 2024

Keywords

  • IoT
  • Data quality
  • Error correction
  • Missed events
  • Noise
  • Process mining
  • Rule-based correction

Fingerprint

Dive into the research topics of 'An Interactive Error-correcting Approach for IoT-sourced Event Logs'. Together they form a unique fingerprint.

Cite this