Aims & Scope

Proceedings of the ACM on Management of Data (PACMMOD) is a journal concerned with the principles, algorithms, techniques, and applications of database management systems, data management technology, and data science and engineering. The journal invites submissions of original research on data management, data engineering, and data science that target the data life cycle of real applications and study phenomena at scales, complexities, and granularities never before possible. This data life cycle often encompasses applications leveraging statistical, machine learning, and artificial intelligence methods and, in many instances, using massive and heterogeneous collections of potentially noisy datasets. Articles that address data challenges at various stages of the life cycle, from modeling, acquisition, cleaning, integration to indexing, querying, analysis, exploration, visualization, interpretation, and explanation fall within the areas of coverage. Papers are generally expected to focus on data-intensive components of data science pipelines, and to solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems), operating within the constraints of accuracy, privacy, fairness, and diversity. Submissions describing deployed systems and solutions to data science pipelines and/or fundamental experiences and insights from evaluating real-world problems are encouraged.