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editorial
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PACMMOD Volume 1, Issue 3: Editorial

Published: 13 November 2023 Publication History

Abstract

We are excited to introduce this new issue of PACMMOD (Proceedings of the ACM on Management of Data). PACMMOD is a new journal, concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. Articles published at PACMMOD address data challenges at various stages of the data lifecycle, from modeling, acquisition, cleaning, integration, indexing, querying, analysis, exploration, visualization, interpretation, and explanation. They focus on dataintensive components of data pipelines, and solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems), operating within accuracy, privacy, fairness, and diversity constraints. Articles reporting deployed systems and solutions to data science pipelines and/or fundamental experiences and insights from evaluating real-world data engineering problems are especially encouraged.

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Published In

cover image Proceedings of the ACM on Management of Data
Proceedings of the ACM on Management of Data  Volume 1, Issue 3
PACMMOD
September 2023
472 pages
EISSN:2836-6573
DOI:10.1145/3632968
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2023
Published in PACMMOD Volume 1, Issue 3

Author Tags

  1. data acquisition
  2. data analysis
  3. data cleaning
  4. data explanation
  5. data exploration
  6. data indexing
  7. data integration
  8. data interpretation
  9. data management
  10. data modeling
  11. data querying
  12. data visualization

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