Data and Information Quality (JDIQ)


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Call for Papers

ACM JDIQ is a multi-disciplinary journal that attracts papers ranging from theoretical research to algorithmic solutions to empirical research to experiential evaluations. Its mission is to publish high impact articles contributing to the field of data and Information Quality (IQ). IQ covers a wide range of dimensions including accuracy and completeness, provenance, lineage and trust, understandability and accessibility.

Expected research contributions can range from modeling and measurement of quality, to improvement of quality with data cleansing methods, to organizational management of quality, to evaluations of quality in real-world problems. The list of suggested topics for ACM JDIQ can be seen at this link.

Manuscripts should be submitted via the submission system:

Article Types

JDIQ welcomes six types of contributions:

Research papers

JDIQ accepts high quality research articles that make a significant and novel contribution to the field of IQ. Papers can range from from theoretical research to algorithmic solutions to empirical research to experiential evaluations. Submissions to ACM JDIQ will be reviewed using the following criteria:

  • Relevance to Data and Information Quality.
  • Significance of the contribution with respect to originality of the problem and novelty of the solution
  • Suitable grounding in theory and the current literature
  • Appropriate research methods
  • Readability and organization of the manuscript

The final version of research papers should be between 20 to 25 single-spaced pages.

A submission based on one or more papers that appeared elsewhere should have major value-added extensions over what appeared previously. Widely disseminated (available on the Web), peer-reviewed conference or workshop papers, in addition to journal papers, are considered publications, but technical reports and CoRR articles (neither of which are peer-reviewed) are not. Nevertheless, all overlapping papers appearing in workshops, proceedings, or newsletters should be disclosed to the Editor-in-Chief.

If a submitted manuscript is based on one or more previous publications by one or more of the authors, it should have at least 30% new content

Such submissions must include a cover letter that provides the following:

  • A detailed breakdown of the sections/pages that have been significantly modified or extended (from prior manuscripts).
  • A list of novel contributions specific to this manuscript.

Please include the cover letter as the first page(s) of the submitted manuscript.

Experience papers

Given the diversity of data quality issues, the broad range of interests of JDIQ authors and readers, and the importance of addressing real world problems, we have decided to include a small number of experience papers to better serve our target audience.

A typical experience paper may be submitted by a practitioner or industrial researcher who has a compelling application or interesting dataset to share with our readers. Another scenario would be a researcher in a computer science or business discipline whose research happens to involve data and data quality challenges. The practitioner may not be interested in extending her research into a full research paper. Similarly, the researcher from a specific domain may have already published a reference paper in a different journal and may wish to publish a shorter companion paper about the specific data quality challenges. An experience paper may also be used to present a teaching case.

We emphasize that experience papers must meet the high expectations of all JDIQ papers and make significant contributions to the discipline, e.g., a compelling application and/or an extensive evaluation and/or results that are generalizable across multiple datasets and/or a strong educational component.

Experience papers must satisfy the following criteria:

  • Title: Mandatory "Experience:" prefix in the title.
  • Page limit: 10 pages with an option of an online-only supplement.
  • Online supplement: All papers may include an online only supplement. This option is of greater importance for experiences papers, to allow the inclusion of data, experimental results, screenshots, questionnaires, etc.
  • Reviewing criteria: A standard reviewing process will be followed with three or more reviewers including both academic and industrial / practitioner reviewers. The following reviewer criteria will be applied:
    • Does the paper specify the data quality problem clearly?
    • Does the paper explore multiple aspects of the current solutions?
    • Do the authors provide details of the shortcomings?
    • Do the authors provide insights into solutions or do they provide a solution? Is it convincing?
    • How generalizable is the problem and solution beyond this specific scenario and domain?

Survey papers

JDIQ also welcomes foundational and survey papers that provide a critical assessment of the state of the art on specific IQ topics while highlighting open research challenges. A paper submitted as research article may be a survey and this should be noted on the cover letter.

Survey papers are not expected to introduce novel solutions with respect to the surveyed topic(s), but should provide original contributions in the systematization and/or analysis of such topic(s). Surveys should also give a historical perspective as well as looking at current efforts and future research directions, ensure a comprehensive coverage of the topic(s), and have an extensive bibliography, including relevant recent papers published in JDIQ.

To enable this, survey papers will normally be allowed up to 40 single-spaced pages, rather than the 20-25 for regular papers. Survey papers must meet the technical quality and significance standards of all JDIQ papers and be authoritative in the surveyed area.

Challenge papers

As part of our editorial mission for the ACM Journal of Data and Information Quality we would like to introduce our readers to open challenges in data quality and spur discussions that will potentially lead to new research and solutions. We ask you to share your expertise and insights with the community. We will collect these contributions as articles to be published in selected issues in an ongoing fashion.

For these reasons, we invite three-page submissions addressing the following:

  • What is an important data and information quality-related challenge facing organizations today?
  • Why is this important?
  • How might this challenge be solved? We expect descriptions of open challenges that are not yet solved.

We expect the vision-type manuscript to describe a particularly challenging problem on the first page and to discuss possible solutions on the second. Of course we want our questions to be very broadly interpreted, ranging across (and beyond) the entire set of JDIQ-relevant topics listed here above.

Challenge papers must satisfy the following criteria:

  • Title: The title should indicate that it is a challenge paper.
  • Page limit: 3 pages with an option of an online-only supplement. In specific cases the extension to 4 pages can be granted. An abstract is not needed
  • Reviewing: We plan a speedy reviewing process with two or more reviewers.

We look forward to insightful manuscripts, invigorating challenges and creative solutions! 

Journal topics

JDIQ welcomes high-quality research contributions from the following areas, but not limited to:

  • Concepts, Methods and Tools
    • Data and information quality metrics and measures
    • Big data quality
    • Data provenance and annotation
    • Metadata quality
    • Data quality frameworks and platforms
    • IQ in Data Integration 
    • IQ in Ontology Management
    • Veracity of information sources
    • User-oriented IQ
  • Measurement, improvement, and assurance of IQ
    • Data wrangling and cleaning and pre-analytics
    • Information integration and fusion
    • Record linkage and entity resolution
    • Privacy preservation and security
    • Reproducibility
    • IQ Assessment
    • Cost/benefit analysis of IQ improvement
  • IQ Domains and Applications
    • Unstructured and semi-structured data
    • Probabilistic, incomplete and uncertain data
    • Web and social media data
    • e-Commerce and data analytics
    • Sensors and streaming data
    • Community input, Pay as You Go, and crowdsourcing
    • IQ and IoT
    • IQ in Clinical Research
    • IQ in Economics
    • IQ in Data Science
  • Organizations and IQ
    • Impact of IQ within business processes
    • Business intelligence and IQ
    • Corporate data governance
    • IQ Policies and standards
    • IQ Education and curriculum development
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