Conference Information
SIGMOD 2024: ACM Conference on Management of Data
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Conference Date:
Santiago, Chile
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Call For Papers

There are three research tracks in SIGMOD 2024:

    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 describing applications, systems, and datasets (e.g. content, creation, quality), along with the underlying practical data management problems and related research challenges. These applications may stem from outside the core data management community (e.g., computer graphics, computer networking) or even from outside computer science (e.g., astronomy, genomics, healthcare), but have clearly demonstrated non-trivial data-centric challenges necessitating novel systems and technologies. Submissions may describe fundamental experiences and insights gained from deployment or detailed evaluation of data-intensive systems in particular application domains. 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.

All research track papers (including special track papers) are subject to double-anonymity requirement.


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

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

SIGMOD welcomes submissions on inter-disciplinary work, as long as there are clear contributions to management of data. 
Last updated by Dou Sun in 2023-08-06
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