The annual ACM SIGMOD conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. We invite the submission of original research contributions relating to all aspects of data management defined broadly, and particularly encourage submissions on topics of emerging interest in the research and development communities.


Abstract submission deadlines: July 12 (Round 1), October 18 (Round 2)

Submission website: https://cmt3.research.microsoft.com/SIGMOD2019

Latest ACM paper format (2017): 14 pages + 4 pages for bibliography/appendix.

The font size needs to be changed to 10 pts.

Authors of accepted papers have the option to upload a video presentation.


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

Benchmarking and performance evaluation

Crowd sourcing

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

Distributed and parallel databases

Graph data management, RDF, social networks

Information extraction

Information retrieval and text mining

Knowledge discovery, clustering, data mining

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

Machine learning methods for management of data

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


All aspects of the submission and notification process will be handled electronically. Submissions must adhere to the paper formatting instructions. Research papers will be judged for quality and relevance through double-blind reviewing, where the identities of the authors are withheld from the reviewers. Author names and affiliations must not appear in the papers, and bibliographic references must be adjusted to preserve author anonymity. Submissions should be uploaded at: https://cmt3.research.microsoft.com/SIGMOD2019.

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