The annual ACM SIGMOD conference is a leading international forum for data management researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences.

There are three research tracks in SIGMOD 2021:

    Data Management Track
    We invite the submission of original research contributions relating to all aspects of data management.
    Data Science and Engineering Track
    We invite the submission of original research in data science and engineering, inspired by real applications. Such papers are expected to focus on data-intensive components of data science pipelines; and solve problems in areas of interest to the community (e.g., data curation, optimization, performance, storage, systems).
    Applications Track
    We invite the submission of novel applications of data management systems and technologies from outside the core data management community (e.g., astronomy, computer graphics, computer networking, genomics).

HIGHLIGHTS

    Paper submission deadlines: Tue July 7, 2020 (Round 1), Tue September 22, 2020 (Round 2)
    Submission website: https://cmt3.research.microsoft.com/SIGMOD2021 (open for submission starting June 23, 2020 for Round 1 and September 8, 2020 for Round 2).
    Submissions must use the latest ACM format in the default 9pt font.
    Data Management submissions must be at most 12 pages plus unlimited number of pages for citations.
    Data Science and Engineering submissions must be at most 8 pages plus unlimited number of pages for citations.
    Applications submissions must be at most 4 pages plus unlimited number of pages for citations.

TOPICS OF INTEREST

Topics of interest include but are not limited to the following:

    Benchmarking and performance evaluation
    Crowdsourcing
    Data models, semantics, query languages
    Data provenance
    Data visualization
    Data warehousing, OLAP, SQL Analytics
    Database monitoring and tuning
    Database security, privacy, access control
    Database usability
    Databases for emerging hardware
    Data Systems and Data Management for Machine Learning
    Distributed and parallel databases
    Graph data management, RDF, social networks
    Information extraction
    Information retrieval and text mining
    Knowledge discovery, clustering, data mining
    Machine learning for data management and data systems
    Query processing and optimization
    Schema matching, data integration, and data cleaning
    Scientific databases
    Semi-structured data
    Spatio-temporal databases
    Storage, indexing, and physical database design
    Streams, sensor networks, complex event processing
    Transaction processing
    Uncertain, probabilistic, and approximate databases
    Very large data science applications/pipelines

SIGMOD welcomes submissions on inter-disciplinary work, as long as there are clear contributions to management of data.