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.

TRACKS

There are three research tracks in SIGMOD 2023:

    Regular Track
    We invite the submission of original research contributions relating to all aspects of data management.
    Data Management for Data Science (DMDS) Special Track
    We invite the submission of original data science research targeting the data life cycle of real applications, studying phenomena at scales, complexities, and granularities never before possible. This data life cycle encompasses databases/data management/data systems/data engineering often leveraging statistical, machine learning, and artificial intelligence methods and, in many instances, using massive and heterogeneous collections of potentially noisy datasets. Such papers are expected to focus on data-intensive components of data science pipelines; and solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems). Submissions are expected to describe (a) deployed solutions to data science pipelines and/or (b) fundamental experiences and insights from evaluating real-world data science problems. We expect that the related systems and/or datasets (including possibly query logs) will be accessible for the data management research community in order to promote future research directions.
    Data-intensive Applications (DIA) Special Track
    We invite the submission of papers, from outside of the data management community, describing applications, systems, and datasets (e.g. content, creation, quality), along with the underlying practical data management problems and related research challenges. These applications stem from outside the core data management community (e.g., computer graphics, computer networking) or even from outside computer science (e.g., astronomy, finance, genomics, healthcare), but have clearly demonstrated non-trivial data-centric challenges necessitating novel systems and technologies. Therefore, papers are expected to have as the primary author a researcher or practitioner from a different research community or application area. Submissions are expected to describe (a) deployed solutions to data-intensive applications and/or (b) fundamental experiences and insights from evaluating real-world data-intensive applications. We expect that the relat ed systems and/or datasets (including possibly query logs) will be accessible for the data management research community in order to promote future research directions.

Regular track papers are subject to double-blind requirement; special track papers are not subject to this requirement (i.e., their review is single-blind).

HIGHLIGHTS

    NEW in 2023 Paper submission deadlines: April 15 (Cycle A), July 15 (Cycle B), October 15 (Cycle C)
    Submission website: https://cmt3.research.microsoft.com/SIGMOD2023
    Submissions must use the latest ACM format in the default 9pt font.
    Regular Track submissions must be at most 12 pages plus an unlimited number of pages for citations.
    Special Track submissions must be at most 8 pages plus an unlimited number of pages for citations.
    For all tracks, one extra page is allowed after the first review to reflect the reviewer feedback.

TOPICS OF INTEREST

We invite submissions relating to all aspects of the data life cycle. Topics of interest include, but are not limited to:

    Benchmarking, database monitoring, and performance tuning
    Cloud data management and HPC
    Crowdsourced and collaborative data management
    Data models and semantics
    Data provenance and workflows
    Data exploration, visualization, query languages, and user interfaces
    Data integration, information extraction, and schema matching
    Data quality, data cleaning, and database usability
    Data warehousing, OLAP, SQL Analytics
    Data security, privacy, and access control
    Data sparsity, boosting, simulated data, and digital twins
    Data platforms for emerging hardware/Emerging hardware for data management
    Data systems for knowledge discovery, data mining, machine learning, and artificial intelligence
    Distributed, decentralized, and parallel data management, distributed ledgers, and blockchainsGraphs, social networks, and semantic web
    Machine learning and artificial intelligence for data management and data systems
    Multimedia and information retrieval
    Query processing and optimization
    Responsible data management and data fairness
    Self-driving databases
    Semistructured, partially structured, and unstructured data
    Sensor networks and IoT
    Spatial data management
    Storage, indexing, and physical database design
    Streams and complex event processing
    Temporal databases
    Transaction processing
    Uncertain, probabilistic, and approximate databases

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