SIGMOD 2019 CALL FOR PAPERS
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.

IMPORTANT DATES (All Deadlines At 5:00pm Pacific Time )
Research papers: as in the previous two SIGMOD conferences, there are two submission deadlines as below. Each submission cycle involves two rounds of reviewing to allow for minor revisions. Papers rejected in the first cycle are not allowed to be re-submitted in the second cycle. All notification dates are approximate.

RESEARCH PAPER FIRST SUBMISSION CYCLE:
July 12, 2018 : Abstract submission
July 19, 2018: Paper submission
October 5, 2018 : Notification of accept/revise/reject
November 2, 2018: Revised Submission
November 26, 2018 : Notification of revision decision

RESEARCH PAPER SECOND SUBMISSION CYCLE:
October 18, 2018 : Abstract submission
October 25, 2018: Paper submission
January 18, 2019 : Notification of accept/revise/reject
February 15, 2019: Revised Submission
March 11, 2019 : Notification of revision decision

CONFERENCE DATES:
June 30 - July 5, 2019


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
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