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Journal of Data and Information Quality is a peer-reviewed journal published by ACM, covering Data Quality and Management, Big Data and Business Intelligence, Privacy-Preserving Technologies in Data. Over its lifetime it has published 409 papers, which have received 6,805 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. Journal of Data and Information Quality 12, 3, Article 45 (2023).
Formats any DOI in Journal of Data and Information Quality style. No sign-up. |
| Publishes research in | Data Quality and Management Big Data and Business Intelligence Privacy-Preserving Technologies in Data Semantic Web and Ontologies Advanced Database Systems and Queries |
| ISSN | 1936-1955 |
| Citation impact (2-yr) | 2.32 |
| h-index | 38 |
| i10-index | 157 |
| Top institutions publishing here | Australian National University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems.…
Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With data governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals…
In this article, we introduce and discuss the pervasive issue of bias in the large language models that are currently at the core of mainstream approaches to Natural Language Processing (NLP). We first introduce data selection bias, that is, the bias caused by the choice of texts that make up a training corpus. Then, we…
This work tackles the perennial problem of reproducible baselines in information retrieval research, focusing on bag-of-words ranking models. Although academic information retrieval researchers have a long history of building and sharing systems, they are primarily designed to facilitate the publication of research papers. As such, these systems are often incomplete, inflexible, poorly documented, difficult to…
The Open Data movement has become a driver for publicly available data on the Web. More and more data—from governments and public institutions but also from the private sector—are made available online and are mainly published in so-called Open Data portals. However, with the increasing number of published resources, there is a number of concerns…
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