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ACM Journal of Data and Information Quality (JDIQ)

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影响因子:
2.9
出版商:
ACM
ISSN:
1936-1955
浏览:
23721
关注:
1

征稿

ACM Journal of Data and Information Quality (JDIQ) is an academic journal published by ACM. (ISSN 1936-1955, impact factor 2.9).

About ACM Journal of Data and Information Quality (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). JDIQ accepts research conducted using a wide variety of methods ranging from positivists to interpretive methods, systems building descriptions, and database theory, as well as statistical analysis, mathematical modeling, quasi experimental methods, hermeneutics, action research, and case study. JDIQ accepts diverse research methods that are customary in different research backgrounds and traditions, both quantitative and qualitative. Research papers also need to provide valuable and relevant implications for applying their findings and solutions in practice. Given the diversity of disciplines and author interests, ACM JDIQ welcomes experience papers, typically submitted by a practitioner or industrial researcher, survey papers that provide a critical assessment of the state of the art on specific IQ topics while highlighting open research challenges, and short challenge papers that describe a major research challenge to the JDIQ community. ACM JDIQ is published on a quarterly basis. It also organizes special issues as part of the journal volume offering. ACM JDIQ special issues draw together a range of contributions on a given theme, and are an important mechanism for presenting a focused collection of significant work from areas of high innovation and activity to the Data Quality community. Starting from 2019, JDIQ welcomes a new type of invited contributions, called on the horizon papers. These manuscripts are written by top researchers in the field of Data Quality. They aim to introduce emerging new topics, their challenging aspects and envisioned solutions. They can be submitted by invitation only.
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