Data Quality Management in the Internet of Things.

Area of Science:

• Computer Science
• Data Science
• Information Systems

Background:

• The Internet of Things (IoT) is increasingly integrated into various applications, highlighting the critical importance of data quality.
• Data quality requirements are context-dependent, varying across applications and organizations.
• Existing methodologies and frameworks offer techniques for defining, assessing, and improving data quality.

Purpose of the Study:

• To survey and compare existing data quality frameworks, methodologies, and international standards for IoT data.
• To provide a comparative analysis based on data types, quality definitions, dimensions, metrics, and assessment choices.
• To assist practitioners and researchers in selecting appropriate IoT data quality management techniques.

Main Methods:

• Systematic literature review of data quality frameworks and methodologies for IoT.
• Comparative analysis of identified resources focusing on key data quality aspects.
• Examination of relevant international standards pertaining to IoT data quality.

Main Results:

• A comprehensive overview of current IoT data quality management approaches.
• Identification of variations in data types, quality definitions, dimensions, and metrics across different frameworks.
• Analysis of the suitability of various assessment dimensions for different IoT contexts.

Conclusions:

• The diversity of IoT data quality requirements necessitates careful selection of management techniques.
• This survey provides a structured comparison to guide the choice of appropriate frameworks, methodologies, and standards.
• The findings aim to streamline the process of implementing effective IoT data quality management.