ACM Journal of Data and Information Quality

Scope & Guideline

Championing Excellence in Data Analytics and Management

Introduction

Explore the comprehensive scope of ACM Journal of Data and Information Quality through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore ACM Journal of Data and Information Quality in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1936-1955
PublisherASSOC COMPUTING MACHINERY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2009 to 2024
AbbreviationACM J DATA INF QUAL / ACM J. Data Inf. Qual.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1601 Broadway, 10th Floor, NEW YORK, NY 10019-7434

Aims and Scopes

The ACM Journal of Data and Information Quality focuses on advancing the understanding and methodologies related to data quality, information management, and the ethical implications of data usage. The journal serves as a platform for researchers and practitioners to share innovative approaches and solutions that address the multifaceted challenges of ensuring high-quality data in various domains.
  1. Data Quality Assessment and Improvement:
    Research on methodologies and frameworks for assessing and enhancing data quality, including techniques for data cleaning, validation, and completeness.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
The ACM Journal of Data and Information Quality is currently witnessing several emerging themes that indicate a shift in focus toward cutting-edge methodologies and interdisciplinary approaches. These trends are shaping the future of data quality research and practice.
  1. 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.
  2. 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.
  3. Human-Centric Data Curation:
    Research emphasizing human involvement in data processes is gaining traction, showcasing the importance of collaborative approaches in enhancing data quality.
  4. 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.
  5. 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

While the journal has consistently emphasized several core areas, certain themes appear to be diminishing in prominence. This shift may reflect changing priorities in the field of data quality and the evolving landscape of data science.
  1. 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.
  2. 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.
  3. 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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