IoT data cleaning techniques: A survey

Abstract

Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data trustworthiness. This paper provides a systematic summary of the two main stages of data cleaning for Internet of Things (IoT) data with time series characteristics, including error data detection and data repairing. In respect to error data detection techniques, it categorizes an overview of quantitative data error detection methods for detecting single-point errors, continuous errors, and multidimensional time series data errors and qualitative data error detection methods for detecting rule-violating errors. Besides, it provides a detailed description of error data repairing techniques, involving statistics-based repairing, rule-based repairing, and human-involved repairing. We review the strengths and the limitations of the current data cleaning techniques under IoT data applications and conclude with an outlook on the future of IoT data cleaning.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 35 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.