ACM Journal of Data and Information Quality
Scope & Guideline
Championing Excellence in Data Analytics and Management
Introduction
Aims and Scopes
- Data Quality Assessment and Improvement:
Research on methodologies and frameworks for assessing and enhancing data quality, including techniques for data cleaning, validation, and completeness. - Human-in-the-Loop Approaches:
Exploration of interactive systems where human expertise is integrated into data curation processes, improving the quality of data through user involvement. - Machine Learning and AI for Data Quality:
Investigating the use of machine learning algorithms and artificial intelligence to automate and improve data quality tasks, such as anomaly detection and data cleansing. - Ethics and Governance in Data Management:
Studies focusing on the ethical considerations and governance frameworks necessary for responsible data usage, including fairness, transparency, and accountability. - Multimodal Data Integration:
Research on combining different types of data (text, images, time series) to enhance data quality and facilitate comprehensive analysis in various applications.
Trending and Emerging
- AI-Driven Data Quality Solutions:
A notable increase in research papers exploring the integration of artificial intelligence and machine learning in data quality tasks, signaling a trend towards automation and intelligent systems. - Focus on Ethical Data Practices:
Emerging themes around ethics and governance in data usage reflect a heightened awareness of the implications of data management, particularly concerning fairness and accountability. - Human-Centric Data Curation:
Research emphasizing human involvement in data processes is gaining traction, showcasing the importance of collaborative approaches in enhancing data quality. - Contextual and Multimodal Data Quality:
There is a growing interest in assessing data quality across various contexts and modalities, highlighting the need for comprehensive frameworks that address the complexities of modern data ecosystems. - Application of Data Quality in Emerging Technologies:
Increased publications relating to data quality in fields like IoT, blockchain, and social media analytics suggest a trend toward exploring data quality challenges in new technological landscapes.
Declining or Waning
- Traditional Data Cleaning Techniques:
There seems to be a decline in papers focusing solely on traditional data cleaning techniques without integrating newer technologies or methodologies, indicating a shift towards more innovative solutions. - Static Data Quality Frameworks:
Research centered on static models for data quality assessment is becoming less frequent, as there is a growing preference for dynamic and adaptive frameworks that can respond to real-time data changes. - Basic Data Management Practices:
Papers discussing foundational data management practices are waning, likely due to a focus on more complex and integrated approaches that incorporate advanced technologies like AI and machine learning.
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